{"id":219187,"date":"2026-09-21T10:09:44","date_gmt":"2026-09-21T10:09:44","guid":{"rendered":"https:\/\/10pearls.com\/uk\/?p=219187"},"modified":"2026-10-05T10:43:01","modified_gmt":"2026-10-05T10:43:01","slug":"alternative-data-in-ai-credit-underwriting","status":"publish","type":"post","link":"https:\/\/10pearls.com\/uk\/blog\/alternative-data-in-ai-credit-underwriting\/","title":{"rendered":"What is Alternative Data in AI Credit Underwriting"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"219187\" class=\"elementor elementor-219187\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-38fa540 e-flex e-con-boxed e-con e-parent\" data-id=\"38fa540\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2111b44 elementor-widget__width-initial indigo-h1 elementor-widget elementor-widget-heading\" data-id=\"2111b44\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Alternative Data in AI Credit Underwriting: Risks &amp; US Regulations<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ddb354b elementor-icon-list--layout-inline elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"ddb354b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"38\" height=\"39\" viewBox=\"0 0 38 39\" fill=\"none\"><path d=\"M19.4436 38.8801C15.6001 38.8789 11.8431 37.7389 8.64699 35.6041C5.45086 33.4693 2.95886 30.4355 1.48561 26.8855C0.0123526 23.3356 -0.376111 19.4287 0.369259 15.6582C1.11463 11.8877 2.96042 8.42249 5.6736 5.70014C7.47665 3.88503 9.6225 2.44641 11.9864 1.46788C14.3503 0.48935 16.8852 -0.00956244 19.4436 0.000138806C22.4602 0.00109609 25.4356 0.700358 28.1368 2.04316C30.838 3.38597 33.1917 5.33586 35.0136 7.74014C33.0388 6.61689 30.8055 6.02753 28.5336 6.03014C24.9544 6.03411 21.5229 7.4577 18.992 9.98858C16.4612 12.5195 15.0376 15.9509 15.0336 19.5301C15.0179 23.042 16.3955 26.4169 18.8643 28.9146C21.3332 31.4123 24.6917 32.829 28.2036 32.8541C28.3116 32.8541 28.4216 32.8541 28.5296 32.8541C30.7221 32.8586 32.8833 32.3337 34.8296 31.3241C33.0206 33.6271 30.7279 35.5046 28.1136 36.8241C25.4253 38.1813 22.455 38.8856 19.4436 38.8801ZM4.7736 6.93014L7.4736 11.5201H9.4536V32.4001H13.7736V6.93014H4.7736Z\" fill=\"white\"><\/path><path d=\"M28.27 10C30.1034 10 31.8957 10.5437 33.4201 11.5623C34.9446 12.5809 36.1327 14.0287 36.8344 15.7225C37.536 17.4164 37.7196 19.2803 37.3619 21.0785C37.0042 22.8767 36.1213 24.5284 34.8249 25.8249C33.5284 27.1213 31.8767 28.0042 30.0785 28.3619C28.2803 28.7196 26.4164 28.536 24.7225 27.8344C23.0287 27.1327 21.5809 25.9446 20.5623 24.4201C19.5437 22.8957 19 21.1034 19 19.27C19.0003 16.8115 19.977 14.4538 21.7154 12.7154C23.4538 10.977 25.8115 10.0003 28.27 10Z\" fill=\"white\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">10Pearls Editorial Team<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">16 min read<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-00c3770 e-flex e-con-boxed e-con e-parent\" data-id=\"00c3770\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-b1afeb8 e-con-full e-flex e-con e-child\" data-id=\"b1afeb8\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2613fa3 elementor-widget elementor-widget-heading\" data-id=\"2613fa3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bfbb838 elementor-widget elementor-widget-text-editor\" data-id=\"bfbb838\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alternative data can help lenders work with millions of underserved thin-file and credit-invisible individuals, especially when combined with AI underwriting. But the new opportunities come with new risks and regulatory exposure, which we discuss in this blog.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fd2cd05 e-con-full e-flex e-con e-child\" data-id=\"fd2cd05\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-852bca7 e-con-full e-flex e-con e-child\" data-id=\"852bca7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-65bcb1f section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"65bcb1f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>About 9.8% of the US adult population was credit \u201cunscored\u201d with insufficient or stale data and 2.7% were credit invisible (no credit record to generate a score from), as per the latest <a href=\"https:\/\/files.consumerfinance.gov\/f\/documents\/cfpb_update-credit-invisibles-estimate_2025-06.pdf\" target=\"_blank\" rel=\"noopener nofollow\"><span style=\"text-decoration: underline;\">Consumer Financial Protection Bureau (CFPB) report<\/span><\/a>. Lenders have started using alternative financial data to score and serve this massive market segment, often in conjunction with AI credit underwriting, because traditional lending models rely on conventional credit scores. While it&#8217;s a boon for millions of underserved US adults that might otherwise not get access to credible loan products, using alternative data in AI underwriting may introduce a new range of regulatory challenges. This includes new bias dimensions, privacy issues, and fair lending concerns.\u00a0\u00a0<\/p><p>This blog aims to offer a broad overview (not legal advice) of the regulatory risks and opportunities tied to the use of alternative data in AI loan underwriting.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-aeed0ed e-con-full e-grid section-head-margin-bottom e-con e-child\" data-id=\"aeed0ed\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-47bda51 elementor-widget elementor-widget-image-box\" data-id=\"47bda51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">9.8%<\/h3><p class=\"elementor-image-box-description\">Of US adults are credit \u201cunscored,\u201d with insufficient or stale data to generate a score<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae0f26f elementor-widget elementor-widget-image-box\" data-id=\"ae0f26f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">2.7%<\/h3><p class=\"elementor-image-box-description\">Of US adults are credit invisible, with no credit record at all<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-00e87dc indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"00e87dc\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"01\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What \"alternative data in AI credit underwriting\" actually means<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d74fbc3 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"d74fbc3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The conventional credit scoring framework naturally concentrates around credit history, habits, and mix. Alternative data uses an individual\u2019s non-credit-related financial data and some non-financial data to evaluate their creditworthiness. The financial data spans across cash flow elements like transaction history, balances, overdrafts, rent, utilities, and telco, and bill payments. It may also cover alternative lending products like Buy Now Pay Later (BNPL). Non-financial data includes <span data-teams=\"true\">behavioural <\/span>data, employment history, education level, etc.<\/p><p>AI credit underwriting is the use of AI to assess credit risk and inform or make lending decisions, drawing on both conventional credit reports and alternative data. AI\u2019s ability to extract relevant data and simultaneously identify patterns and anomalies from both structured and unstructured data sources makes it a strong fit for automating and accelerating the credit underwriting process. Lenders <span data-teams=\"true\">leveraging <\/span>AI loan underwriting can decide whether to stop the process at recommendation or automate lending decisions as well.<\/p><p>For example, a lender might assess a thin-file applicant using twelve months of bank-transaction history showing steady income and on-time rent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c3e25a0 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"c3e25a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The subtle risk<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47eaebb section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"47eaebb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Using alternative data for credit scoring introduces new regulatory risks, especially with non-financial data, which may lead to biased decisions. This is true even with purely human-based decision-making because some variables may correlate with protected classes, leading to discriminatory decisions that may be statistically sound by the lender&#8217;s own rubric yet still produce discriminatory outcomes that carry fair-lending and legal exposure, not just ethical concerns.<br \/><br \/>This is further compounded by using alternative data in AI underwriting, because it may identify and reinforce patterns and trends that may converge in certain borrower subsets and, without adequate controls, may make financially sound but biased recommendations for credit decision-making.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-838d683 section-head-margin-bottom elementor-widget elementor-widget-image\" data-id=\"838d683\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-1024x683.webp\" class=\"attachment-large size-large wp-image-219193\" alt=\"\" srcset=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-1024x683.webp 1024w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-300x200.webp 300w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-768x512.webp 768w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-1536x1024.webp 1536w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-1-2048x1366.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9a3a274 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"9a3a274\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"02\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The opportunity<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-297a0d3 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"297a0d3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>AI credit underwriting carries risk, but it also offers significant opportunities, including:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3043acd indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"3043acd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Financial inclusion<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a8bbc4a section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"a8bbc4a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>If the CFPB&#8217;s latest percentages of credit invisible (2.7%) and unscored US adults (9.8%) hold for the current population, there are about 33 million adults in the US without a credit score. This prevents access to a wide range of conventional credit products \u2013 from personal loans to mortgages. Using alternative credit data, ideally along with AI credit scoring, can help these individuals access a broader range of credit products.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fdcaa28 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"fdcaa28\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Better risk modelling<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-085d223 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"085d223\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Risk modelling is among the most mature <a href=\"\/machine-learning\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\"> AI and Machine Learning (ML) domains<\/span><\/a>, with proven results across multiple lending categories. One example is Klarna, a BNPL giant that has leveraged ML to improve its credit underwriting model with 2x better default predictability compared to the VantageScore benchmark, a widely used US scoring model. AI credit decisioning can leverage alternative data points and identify cash flow and financial <span data-teams=\"true\">behaviour <\/span>patterns that classic credit data misses, enabling lenders to approve creditworthy borrowers that traditional scores overlook, while flagging high-scoring applicants who are likely to default.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d1ec220 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"d1ec220\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Speed &amp; automation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d0f12cb section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"d0f12cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Conventional underwriting often requires employees to collect documents, verify income, review statements, <span style=\"text-decoration: underline;\"><a href=\"\/rpa-services\/\" target=\"_blank\" rel=\"noopener\">compare information across systems<\/a><\/span> and enter the same data more than once.<\/p><p>AI-automated underwriting can reduce that work. A system can <a href=\"\/nlp\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\">classify documents, extract income and cash-flow data<\/span><\/a>, compare the information with the application and return a recommendation or decision within minutes.<\/p><p>Straightforward approvals and declines may be processed automatically. Underwriters can then spend more time on exceptions, ambiguous cases and applications requiring <span data-teams=\"true\">judgement<\/span>.<\/p><p>Borrowers receive decisions sooner. Lenders can reduce processing costs and increase capacity without removing the controls that make underwriting dependable.<\/p><p>Speed, however, should be the result of a well-designed process. It should not come from skipping verification, explanation or review.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f57ebcd indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"f57ebcd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">New segments<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c7e7e76 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"c7e7e76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Traditional scoring struggles with anyone who lacks a long credit history in the US, even when they are creditworthy. Immigrants arrive in the US with a blank profile regardless of their credit history in their home country; roughly 1.3 million immigrants came to the US between July 2024 and June 2025, and millions of Americans turn 18 each year, also starting with a blank &#8220;credit&#8221; slate.<\/p><p>Self-employed and <span style=\"text-decoration: underline;\"><a href=\"https:\/\/www.consumerfinance.gov\/data-research\/research-reports\/the-financial-security-of-small-business-owners-evidence-from-the-making-ends-meet-survey\/\" target=\"_blank\" rel=\"noopener nofollow\">small business owners<\/a><\/span> are underserved too, mostly because of income volatility, which restricts their access to credit. Alternative data reaches these borrowers by reading what conventional scores miss \u2013 cash flow, including rent, bill payments, and business revenue.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c4e29c2 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"c4e29c2\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"03\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The data: What counts as alternative data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fca1ed section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"3fca1ed\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alternative data is not only about how much lenders can access, on their own or through alternative credit data providers. <a href=\"\/blog\/navigating-1033-compliance-in-fintech\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\">Open banking has enriched these datasets<\/span><\/a> enormously, but having the right data matters far more than having more of it, since volume alone can simply introduce noise. The right sources let lenders derive thousands of <span data-teams=\"true\">behavioural <\/span>signals to assess a borrower&#8217;s risk profile and creditworthiness. <span class=\"TextRun SCXW188110220 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW188110220 BCX8\">The categories below differ sharply in what they reveal and where they come from, and they\u00a0<\/span><span class=\"NormalTextRun SCXW188110220 BCX8\">demonstrate<\/span><span class=\"NormalTextRun SCXW188110220 BCX8\">\u00a0that even the right data can carry regulatory and ethical risks that lenders and fintechs building\u00a0<\/span><\/span><a href=\"\/custom-fintech-solutions\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\"><span class=\"TextRun Underlined SCXW188110220 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW188110220 BCX8\">custom fintech solutions<\/span><\/span><\/span><\/a><span class=\"TextRun SCXW188110220 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW188110220 BCX8\">\u00a0need to understand.<\/span><\/span><span class=\"EOP Selected SCXW188110220 BCX8\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:276}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a1407a mi-table section-head-margin-bottom elementor-widget elementor-widget-html\" data-id=\"6a1407a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t\t<div class=\"mi-table\">\r\n  <table>\r\n    <thead>\r\n      <tr>\r\n        <th>Data type<\/th>\r\n        <th>What it signals<\/th>\r\n        <th>Typical source or provider<\/th>\r\n        <th>Coverage &amp; data quality<\/th>\r\n        <th>Risk flag<\/th>\r\n      <\/tr>\r\n    <\/thead>\r\n    <tbody>\r\n      <tr>\r\n        <td><strong>Cash-flow & bank-transaction<\/strong><\/td>\r\n        <td>Income stability, spending patterns, the ability to repay<\/td>\r\n        <td>Open banking aggregators (Like Plaid)<\/td>\r\n        <td>Wide and improving via open banking; high quality<\/td>\r\n        <td>Low<\/td>\r\n      <\/tr>\r\n      <tr>\r\n        <td><strong>Rent payments<\/strong><\/td>\r\n        <td>Consistent large recurring obligation met on time<\/td>\r\n        <td>Rent-reporting services, property managers<\/td>\r\n        <td>Inconsistent; reporting is voluntary and incomplete<\/td>\r\n        <td>Low<\/td>\r\n      <\/tr>\r\n      <tr>\r\n        <td><strong>Utility & telecom<\/strong><\/td>\r\n        <td>Bill-payment reliability, financial routine<\/td>\r\n        <td>Utility and telco providers, specialist bureaus<\/td>\r\n        <td>Uneven; varies by provider and region<\/td>\r\n        <td>Low to moderate<\/td>\r\n      <\/tr>\r\n      <tr>\r\n        <td><strong>Employment & income verification<\/strong><\/td>\r\n        <td>Current income and job stability<\/td>\r\n        <td>Payroll and verification services<\/td>\r\n        <td>Good where employer data is accessible<\/td>\r\n        <td>Low<\/td>\r\n      <\/tr>\r\n      <tr>\r\n        <td><strong>Public records<\/strong><\/td>\r\n        <td>Property, liens, and other formal financial events<\/td>\r\n        <td>Government and public-record aggregators<\/td>\r\n        <td>Broad but shallow; limited predictive depth<\/td>\r\n        <td>Low<\/td>\r\n      <\/tr>\r\n      <tr>\r\n        <td><strong>Behavioural device data<\/strong><\/td>\r\n        <td>Correlated (not causal) signals of risk<\/td>\r\n        <td>Digital-footprint and device-analytics vendors<\/td>\r\n        <td>Abundant but opaque<\/td>\r\n        <td>High: proxy-discrimination and privacy exposure<\/td>\r\n      <\/tr>\r\n    <\/tbody>\r\n  <\/table>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d8cc922 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"d8cc922\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"04\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The risks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1926e60 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"1926e60\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alternative data widens the lender\u2019s view of an applicant, but a wider view is not automatically more accurate or fair.<br \/><br \/>The information may be incomplete, collected without meaningful understanding, or correlated with characteristics that should not influence a credit decision. Those concerns become more significant when models process thousands of variables and produce decisions faster than people can review them.<br \/><br \/>A candid and pragmatic view of AI credit underwriting must balance the opportunity with these risks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-28e11f0 e-con-full e-grid section-head-margin-bottom e-con e-child\" data-id=\"28e11f0\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-03a35c5 elementor-widget elementor-widget-image-box\" data-id=\"03a35c5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Bias and proxy discrimination<\/h3><p class=\"elementor-image-box-description\">Alternative data can act as a proxy, a stand-in for several protected characteristics in credit decisions, even when those characteristics are never used. ZIP code, device type, spending patterns, and rental history can all correlate with race, age, or nationality, so a model can produce discriminatory outcomes without any protected variable being used directly. The harm can be significant, and the intent unfortunately is irrelevant, which is precisely what makes proxy discrimination hard to catch.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b12348 elementor-widget elementor-widget-image-box\" data-id=\"5b12348\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Explainability and the black-box problem<\/h3><p class=\"elementor-image-box-description\">Many machine learning models are black boxes and their decision-making rationale cannot be tracked or explained. In lending, that is a compliance and trust problem, not just a technical limitation. If a lender cannot explain exactly why an applicant was declined, it cannot produce the specific, accurate reason codes the law requires, which leaves it unable to defend a challenged decision.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e59fb84 elementor-widget elementor-widget-image-box\" data-id=\"e59fb84\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Data quality, coverage, and accuracy<\/h3><p class=\"elementor-image-box-description\"><a href=\"\/blog\/ai-data-readiness-roadmap-guidance\/\" target=\"_blank\" rel=\"noopener noreferrer\">Alternative data is inherently uneven<\/a>. Some sources are thin, stale, or wrong, and these coverage gaps impact the most vulnerable borrowers, which the alternative data was actually meant to help. An error in a traditional credit file can be handled with established dispute mechanisms. But an error in a novel data source may not have such a recourse, and the impacted borrower may never learn about it.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-420de31 elementor-widget elementor-widget-image-box\" data-id=\"420de31\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Privacy, consent, and security<\/h3><p class=\"elementor-image-box-description\">A lot of alternative data is intimate and is either directly drawn from bank transactions, phone records, and online behaviour or inferred from data points and patterns. Its use raises real questions about whether borrowers meaningfully consented to it because even if they consent to share data points, they may not understand how much can be inferred from it. Concentrating this data also <a href=\"\/security-services\/cyber-security\/\" target=\"_blank\" rel=\"noopener noreferrer\">amplifies the breach risk<\/a>, turning an underwriting advantage into a security liability.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f43a3f elementor-widget elementor-widget-image-box\" data-id=\"3f43a3f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Regulatory and fair-lending exposure<\/h3><p class=\"elementor-image-box-description\">Each of these issues ultimately becomes a governance concern. Lenders remain responsible for avoiding unlawful discrimination, producing valid explanations, maintaining accurate information and controlling third parties. Those obligations do not disappear because a model is technically sophisticated or supplied by a specialist vendor.\n\nThe legal routes through which a claim may be brought can change. The need to understand and defend the decision remains.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f8c7d0 elementor-widget elementor-widget-image-box\" data-id=\"6f8c7d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Vendor and third-party dependency<\/h3><p class=\"elementor-image-box-description\">Much of the alternative data market depends on outside providers. A lender may inherit a vendor\u2019s weaknesses in consent, collection, identity matching, data quality and coverage. Changes to a provider\u2019s methodology or access to a source can alter model behaviour without an obvious change to the lender\u2019s own system. This makes vendor management part of model governance. A contractual relationship does not transfer accountability away from the institution making the credit decision.<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33d3b55 elementor-widget elementor-widget-image-box\" data-id=\"33d3b55\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t\t<div class=\"elementor-image-box-wrapper\"><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\">Model drift and overfitting<\/h3><p class=\"elementor-image-box-description\">A model tuned on past behaviour degrades as conditions change. Overfitting makes it look accurate in testing and unreliable in production, and drift means a model that was fair and accurately predictive at launch can deviate in production, unless it's <a href=\"\/blog\/streamlining-development-workflows-by-leveraging-mlops\/\" target=\"_blank\" rel=\"noopener noreferrer\">actively monitored<\/a> and \"course corrected.\"<\/p><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1dae5de section-head-margin-bottom elementor-widget elementor-widget-image\" data-id=\"1dae5de\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-1024x576.webp\" class=\"attachment-large size-large wp-image-219194\" alt=\"\" srcset=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-1024x576.webp 1024w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-300x169.webp 300w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-768x432.webp 768w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-1536x864.webp 1536w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-fold-2-2048x1152.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9709e8c indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"9709e8c\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"05\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Regulatory exposure: \nThe 2026 US compliance map<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73349cd section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"73349cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The US rules governing AI credit underwriting shifted in 2026, and the shift is easy to misread. The federal fair-lending lane narrowed, but the obligations that matter most for alternative data are still in force.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-83815d5 e-con-full e-flex e-con e-child\" data-id=\"83815d5\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9cc3778 indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"9cc3778\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tECOA and Regulation B\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tThe <a href=\"https:\/\/www.federalreserve.gov\/boarddocs\/supmanual\/cch\/fair_lend_reg_b.pdf\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">Equal Credit Opportunity Act<\/a> (ECOA) prohibits discrimination in any credit transaction, regardless of the technology used to make the decision. A machine producing the output changes nothing about that.\u00a0\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a4d0797 e-con-full e-flex e-con e-child\" data-id=\"a4d0797\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c96b735 indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"c96b735\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tAdverse action notices\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tWhen a lender denies credit or takes another adverse action, it must provide specific and accurate reasons or explain how the applicant may obtain them. Complexity is not an exemption. The reasons provided must correspond to the factors actually considered or scored by the creditor. A broad label that does not reflect the real cause of the decision is insufficient.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e35a19b e-con-full e-flex e-con e-child\" data-id=\"e35a19b\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a46ec02 indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"a46ec02\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tThe April 2026 change\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tDisparate impact is the idea that a policy can be discriminatory if it produces unequal outcomes across protected groups, even when there was no intent to discriminate, and no protected characteristic was used directly. On April 22, 2026, the CFPB published <a href=\"https:\/\/www.federalregister.gov\/documents\/2026\/04\/22\/2026-07804\/equal-credit-opportunity-act-regulation-b\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">a final rule<\/a> amending Regulation B that eliminates disparate impact, the \"effects test,\" as a theory of liability under ECOA. The rule takes effect July 21, 2026, and refocuses federal ECOA enforcement on statutory text and evidence of intentional discrimination rather than effects-based analysis. This is a significant narrowing of one federal enforcement channel.\u00a0\u2028\u2028\n<br><br>\nBut disparate impact exposure has not disappeared. The rule changes federal ECOA enforcement only. It does not touch the Fair Housing Act, which independently recognises disparate impact in mortgage lending, nor state anti-discrimination laws, several of which still recognise it. For example, New York Department of Financial Services reminded lenders, the day the rule was published, that decisions producing a disparate impact may still be unlawful. Fair-lending testing remains a live expectation.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1d51df3 e-con-full e-flex e-con e-child\" data-id=\"1d51df3\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9c2d11f indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"9c2d11f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tFCRA\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tWhen a provider assembles alternative data for credit decisions, that activity can bring the data within the Fair Credit Reporting Act's definition of a consumer report. This triggers certain accuracy and dispute obligations. Regulators addressed alternative data in underwriting directly in a <a href=\"https:\/\/www.federalreserve.gov\/supervisionreg\/caletters\/ca%2019-11%20letter%20attachement%20interagency%20statement%20on%20the%20use%20of%20alternative%20data%20in%20credit%20underwriting.pdf\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">2019 interagency statement<\/a>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1bb2e9e e-con-full e-flex e-con e-child\" data-id=\"1bb2e9e\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-81141fa indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"81141fa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tPrivacy\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tThe Gramm-Leach-Bliley Act (GLBA) governs the handling and protection of nonpublic personal information by covered financial institutions. State privacy laws add another layer, with varying requirements. The overall theme is that more data creates more data governance responsibility for lenders.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6ed62b2 e-con-full e-flex e-con e-child\" data-id=\"6ed62b2\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-39a1df8 indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"39a1df8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tModel risk\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tIn April 2026, the OCC, Federal Reserve, and FDIC issued revised guidance, <a href=\"https:\/\/www.occ.gov\/news-issuances\/bulletins\/2026\/bulletin-2026-13.html\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">OCC Bulletin 2026-13<\/a>, superseding the long-standing SR 11-7 framework. It keeps the core expectations, independent validation, ongoing monitoring, documentation, board oversight, and signals AI-specific guidance is likely to follow.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3ca6523 e-con-full e-flex e-con e-child\" data-id=\"3ca6523\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c3cdd67 indigo-h4 elementor-widget elementor-widget-icon-box\" data-id=\"c3cdd67\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tState AI laws\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tA fast-moving layer sits on top of this. Several states are enacting broad AI regulations that may impact lending decisions and lenders' liability. The Colorado AI Act is a leading example. These laws are broad rather than credit-specific, but aspects of them may influence AI credit underwriting, and the landscape is changing quickly enough to warrant ongoing attention.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6898e51 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"6898e51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>It&#8217;s important to keep in mind that this is an overview of the current regulatory environment affecting alternative data and AI credit underwriting, not legal advice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6657b15 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"6657b15\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Capturing the opportunity without the exposure<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8941a01 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"8941a01\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span class=\"TextRun SCXW229468112 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229468112 BCX8\">The risks are real, but they are manageable. Each one requires a specific responsible AI approach, and <\/span><\/span><span style=\"text-decoration: underline;\"><a href=\"\/artificial-intelligence\/ai-governance-consulting\/\" target=\"_blank\" rel=\"noopener\"><span class=\"TextRun Underlined SCXW229468112 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229468112 BCX8\">ai governance<\/span><span class=\"NormalTextRun SCXW229468112 BCX8\"> consulting <\/span><span class=\"NormalTextRun SCXW229468112 BCX8\">services<\/span><\/span><\/a><\/span><span class=\"TextRun SCXW229468112 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229468112 BCX8\"> are how lenders turn alternative data from a liability into an advantage.<\/span><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8b20d78 e-con-full section-head-margin-bottom e-flex e-con e-child\" data-id=\"8b20d78\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0ac3b91 e-con-full e-flex e-con e-child\" data-id=\"0ac3b91\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c2f2668 counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"c2f2668\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2ef4bd9 e-flex e-con-boxed e-con e-child\" data-id=\"2ef4bd9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b3f8020 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"b3f8020\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Use explainable models and real reason codes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5642c89 elementor-widget elementor-widget-text-editor\" data-id=\"5642c89\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Select models whose decisions can be traced and generate specific reason codes mapped to the actual factors behind each decision. If a model cannot produce an honest, factor-level explanation, it is not ready for a lending decision.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a9b57cc e-con-full e-flex e-con e-child\" data-id=\"a9b57cc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2e6e372 counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"2e6e372\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-648f62c e-flex e-con-boxed e-con e-child\" data-id=\"648f62c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d8e550a indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"d8e550a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Test for fair lending continuously<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3ef2cc elementor-widget elementor-widget-text-editor\" data-id=\"d3ef2cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Run disparate-impact testing as an ongoing practice and document a search for Less Discriminatory Alternatives (LDA). This is a pragmatic practice despite the federal pivot since it remains a live expectation under the Fair Housing Act for AI mortgage underwriting and under state law elsewhere.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-53ea33f e-con-full e-flex e-con e-child\" data-id=\"53ea33f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-08a50d3 counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"08a50d3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a932bda e-flex e-con-boxed e-con e-child\" data-id=\"a932bda\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4003122 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"4003122\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Govern the data itself<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45bd181 elementor-widget elementor-widget-text-editor\" data-id=\"45bd181\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Maintain an inventory of every alternative source, including its owner, purpose, provenance, consent basis, quality, coverage, retention period, and its permitted uses. Data that cannot be traced is difficult to validate. Data that cannot be justified is difficult to defend.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-dd70591 e-con-full e-flex e-con e-child\" data-id=\"dd70591\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8f88c5f counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"8f88c5f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-44fb824 e-flex e-con-boxed e-con e-child\" data-id=\"44fb824\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa1d577 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"aa1d577\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Map every output to adverse-action requirements<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b53316 elementor-widget elementor-widget-text-editor\" data-id=\"1b53316\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Before a model goes live, confirm that each possible decision can produce the specific, accurate notice that the law requires. Explainability is only useful if it corresponds to the relevant compliance elements.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b883988 e-con-full e-flex e-con e-child\" data-id=\"b883988\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8a57b78 counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"8a57b78\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9a91b3a e-flex e-con-boxed e-con e-child\" data-id=\"9a91b3a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5399444 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"5399444\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stand up model risk management<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1f1ca9 elementor-widget elementor-widget-text-editor\" data-id=\"e1f1ca9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Apply independent validation, ongoing monitoring, and documentation in line with the 2026 interagency guidance in OCC Bulletin 2026-13. A model that isn&#8217;t revalidated is at risk of compliance complications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e945598 e-con-full e-flex e-con e-child\" data-id=\"e945598\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-777b3cb counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"777b3cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-583f862 e-flex e-con-boxed e-con e-child\" data-id=\"583f862\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-854c5cc indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"854c5cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Keep humans in the loop for consequential decisions<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10add00 elementor-widget elementor-widget-text-editor\" data-id=\"10add00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Automate the routine, high-confidence cases where the policy and evidence are clear. <a href=\"\/blog\/ai-agent-authorization\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\">Divert any unusual, low-confidence, or high-impact decisions<\/span><\/a> to human experts with the knowledge and expertise for a meaningful review.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0204929 e-con-full e-flex e-con e-child\" data-id=\"0204929\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f545430 counter-icon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f545430\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-star\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b7d18de e-flex e-con-boxed e-con e-child\" data-id=\"b7d18de\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-426147f indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"426147f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Do real vendor due diligence<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96599cc elementor-widget elementor-widget-text-editor\" data-id=\"96599cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Examine the data and model providers you depend on, their sources, consent practices, and validation, because their weaknesses become yours. Responsible AI credit underwriting is ultimately a supply-chain problem as much as a modelling one.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53ce82e indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"53ce82e\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"06\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What this means for lenders now<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2375bde section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"2375bde\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alternative data and AI credit underwriting will continue to grow because the commercial and inclusion opportunity is too significant to ignore.<\/p><p>Thin-file borrowers, immigrants, young adults, self-employed workers and small-business owners represent large populations whose risk cannot always be understood through conventional scores alone. Better data and better modelling can help lenders serve them <br \/>more confidently.<\/p><p>The 2026 regulatory changes do not remove the need for governance. They simply redistribute the exposure across ECOA intent-based claims, adverse-action requirements, the Fair Housing Act, FCRA, privacy rules, state laws, and internal model controls.<\/p><p>The lenders have two different paths in front of them. The ones who treat governance as an afterthought will keep meeting the same bias, explainability, and compliance problems downstream, where they are most expensive to fix. The ones who build explainability, fair-lending testing, and data governance into the AI credit systems from day one will be the ones who can actually scale AI lending and AI credit decisioning and are able to defend it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59c78bc indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"59c78bc\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"07\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-254758b section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"254758b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Alternative data and AI credit underwriting can widen access to credit while improving lenders\u2019 understanding of risk.<\/p><p>Neither outcome is automatic.<\/p><p>The value depends on disciplined data selection, explainable decisions, fair-lending testing and governance that reflects the changing US regulatory environment. The objective should not be to collect the most information or deploy the most complex model. It should be to make a more informed credit decision that the institution can support, explain and monitor.<\/p><p><span class=\"TextRun SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">At\u00a0<\/span><\/span><a href=\"\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\"><span class=\"TextRun Underlined SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">10Pearls<\/span><\/span><\/span><\/a><span class=\"TextRun SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">, our\u00a0<\/span><\/span><span style=\"text-decoration: underline;\"><a href=\"\/industry-financial-services\/\" target=\"_blank\" rel=\"noopener\"><span class=\"TextRun Underlined SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">fintech software development services<\/span><\/span><\/a><\/span><span class=\"TextRun SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">\u00a0teams help financial institutions put that discipline into practice. Our work spans data engineering, AI and machine learning, model governance, and\u00a0<\/span><\/span><span class=\"TextRun Underlined SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\"><a href=\"\/artificial-intelligence\/ai-integration-services\/\" target=\"_blank\" rel=\"noopener\"><span style=\"text-decoration: underline;\">ai integration<\/span><\/a> services<\/span><\/span><span class=\"TextRun SCXW2163105 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW2163105 BCX8\">\u00a0for explainable decision systems designed for regulated environments.<\/span><\/span><span class=\"EOP Selected SCXW2163105 BCX8\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:276}\">\u00a0<\/span><\/p><p>For lenders evaluating where alternative data belongs in their underwriting process, the <br \/>right starting point is not the model alone. It is the operating, data and governance foundation around it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b22f958 indigo-h2 section-head-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"b22f958\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b429780 section-head-margin-bottom elementor-widget-mobile__width-initial elementor-widget elementor-widget-n-accordion\" data-id=\"b429780\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1880\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-1880\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What is alternative data in AI credit underwriting? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1880\" class=\"elementor-element elementor-element-9b0fbce e-con-full e-flex e-con e-child\" data-id=\"9b0fbce\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ecb7b10 elementor-widget elementor-widget-text-editor\" data-id=\"ecb7b10\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>It is the use of AI models to assess credit risk using both traditional credit data and alternative data, information beyond the credit bureau file, such as cash flow, rent, utility, and telecom payments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1881\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1881\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> Is using alternative data for credit decisions legal in the US? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1881\" class=\"elementor-element elementor-element-9ef8cba e-con-full e-flex e-con e-child\" data-id=\"9ef8cba\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-30fa081 elementor-widget elementor-widget-text-editor\" data-id=\"30fa081\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Yes, but its use is regulated. Lenders must comply with ECOA, the FCRA, and other fair-lending requirements. Decisions must be non-discriminatory, supported by reliable data and accompanied by specific, accurate reasons when adverse action is taken.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1882\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1882\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What are the main risks of AI credit underwriting? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1882\" class=\"elementor-element elementor-element-3f55338 e-con-full e-flex e-con e-child\" data-id=\"3f55338\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d8800c0 elementor-widget elementor-widget-text-editor\" data-id=\"d8800c0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The main risks include bias and proxy discrimination, limited explainability, poor or incomplete data, privacy and consent concerns, model drift, third-party dependency, and the broader fair-lending exposure created by these issues.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1883\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1883\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> Does alternative data reduce or increase bias in lending? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1883\" class=\"elementor-element elementor-element-da06df9 e-con-full e-flex e-con e-child\" data-id=\"da06df9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-01bb9e0 elementor-widget elementor-widget-text-editor\" data-id=\"01bb9e0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>It can do either. Well-governed, it can widen fair access; poorly governed, alternative data can encode proxies for protected characteristics and amplify bias. Governance, not the data alone, decides which.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1884\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1884\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What regulations apply to AI credit underwriting in 2026? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1884\" class=\"elementor-element elementor-element-30fd327 e-con-full e-flex e-con e-child\" data-id=\"30fd327\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-13568f2 elementor-widget elementor-widget-text-editor\" data-id=\"13568f2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>ECOA and Regulation B, the FCRA, the Fair Housing Act for mortgages, model-risk guidance (OCC Bulletin 2026-13), privacy laws like GLBA, and a growing layer of state AI laws.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1885\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1885\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> Did the CFPB weaken fair-lending rules for AI underwriting in 2026? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1885\" class=\"elementor-element elementor-element-19eaf3c e-con-full e-flex e-con e-child\" data-id=\"19eaf3c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6f48937 elementor-widget elementor-widget-text-editor\" data-id=\"6f48937\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The federal ECOA enforcement was readjusted in April 2026 to drop disparate-impact liability via a new rule. But the Fair Housing Act and state laws still <span data-teams=\"true\">recognise <\/span>it, so fair-lending obligations are still very much alive.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1886\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"7\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1886\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What is the credit-invisible population? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1886\" class=\"elementor-element elementor-element-74ca633 e-con-full e-flex e-con e-child\" data-id=\"74ca633\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-48aa01f elementor-widget elementor-widget-text-editor\" data-id=\"48aa01f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>People with no credit record at all. As per the CFPB, about 2.7% of US adults are credit invisible, with a further 9.8% unscorable, together roughly 33 million people.\u00a0\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ade8b8f e-con-full e-flex e-con e-child\" data-id=\"ade8b8f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6747b88 elementor-widget elementor-widget-heading\" data-id=\"6747b88\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<div class=\"elementor-heading-title elementor-size-default\">TABLE OF CONTENTS<\/div>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37e8add elementor-widget elementor-widget-heading\" data-id=\"37e8add\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#01\">What \"alternative data in AI credit underwriting\" actually means <\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d78294 elementor-widget elementor-widget-heading\" data-id=\"0d78294\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#02\">The opportunity\u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b10a60b elementor-widget elementor-widget-heading\" data-id=\"b10a60b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#03\">The data: What counts as alternative data\u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff681f4 elementor-widget elementor-widget-heading\" data-id=\"ff681f4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#04\">The risks\u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-edfcce3 elementor-widget elementor-widget-heading\" data-id=\"edfcce3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#05\">Regulatory exposure: The 2026 US compliance map\u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8627f13 elementor-widget elementor-widget-heading\" data-id=\"8627f13\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#06\">What this means for lenders now\u00a0 \u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9f6d49 elementor-widget elementor-widget-heading\" data-id=\"a9f6d49\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#07\">Conclusion\u00a0<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1008dbf section-padding e-flex e-con-boxed e-con e-parent\" data-id=\"1008dbf\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-08b5edc indigo-h2 section-head-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"08b5edc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related blogs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-edf3862 elementor-widget elementor-widget-insights_section_widget\" data-id=\"edf3862\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"insights_section_widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n\t\t\/* \u2500\u2500 Widget wrapper \u2500\u2500 *\/\n\t\t#isw-edf3862 {\n\t\t\tposition: relative;\n\t\t\tpadding: 0 40px; \/* space for arrows on left\/right *\/\n\t\t\tbox-sizing: border-box;\n\t\t}\n\t\t#isw-edf3862 .isw-outer {\n\t\t\tposition: relative;\n\t\t\toverflow: hidden;\n\t\t\tpadding: 20px 0;\n\t\t\tmargin: -20px 0;\n\t\t}\n\t\t#isw-edf3862 .isw-track {\n\t\t\tdisplay: flex;\n\t\t\tflex-direction: row;\n\t\t\tflex-wrap: nowrap;\n\t\t\tgap: 20px;\n\t\t\ttransition: transform .5s cubic-bezier(.25,.46,.45,.94);\n\t\t\twill-change: transform;\n\t\t\talign-items: stretch;\n\t\t\tvisibility: hidden;\n\t\t}\n\t\t#isw-edf3862 .isw-slide { flex-shrink: 0; box-sizing: border-box; display: flex; }\n\n\t\t\/* \u2500\u2500 Card \u2014 no shadow by default, only on hover \u2500\u2500 *\/\n\t\t#isw-edf3862 .isw-card {\n\t\t\tborder-radius: 16px; overflow: hidden; width: 100%; flex: 1;\n\t\t\tposition: relative; height: 380px; background: #fff;\n\t\t\tbox-shadow: none;\n\t\t\ttransition: box-shadow .3s ease;\n\t\t}\n\t\t#isw-edf3862 .isw-card:hover {\n\t\t\tbox-shadow: 0px 14px 36px -8px rgba(99,102,241,0.2);\n\t\t}\n\n\t\t\/* Layer 1 \u2014 Image *\/\n\t\t#isw-edf3862 .isw-img-wrap {\n\t\t\tposition: absolute; top: 0; left: 0;\n\t\t\twidth: 100%; height: 100%; z-index: 1;\n\t\t}\n\t\t#isw-edf3862 .isw-img-wrap img {\n\t\t\twidth: 100%; height: 100%; display: block; object-fit: cover;\n\t\t}\n\n\t\t\/* Layer 2 \u2014 Text bar *\/\n\t\t#isw-edf3862 .isw-text-wrap {\n\t\t\tposition: absolute; bottom: 0; left: 0;\n\t\t\twidth: 100%; height: 35%; z-index: 2;\n\t\t\tbackground: #fff; padding: 16px 24px 20px;\n\t\t\tbox-sizing: border-box; display: flex; flex-direction: column;\n\t\t\tgap: 8px; overflow: hidden;\n\t\t\ttransition: height .35s cubic-bezier(.4,0,.2,1);\n\t\t}\n\t\t#isw-edf3862 .isw-card:hover .isw-text-wrap { height: 100%; }\n\n\t\t#isw-edf3862 .isw-excerpt-wrap,\n\t\t#isw-edf3862 .isw-readmore-wrap { display: none; }\n\t\t#isw-edf3862 .isw-card:hover .isw-excerpt-wrap { display: block; }\n\t\t#isw-edf3862 .isw-card:hover .isw-readmore-wrap { display: block; margin-top: auto; }\n\n\t\t\/* \u2500\u2500 Variation 2 overrides \u2500\u2500 *\/\n\t\t#isw-edf3862.isw-v2 .isw-card {\n\t\t\theight: 530px;\n\t\t\tborder-radius: 15px;\n\t\t\tbox-shadow: none;\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-card:hover {\n\t\t\tbox-shadow: 0px 14px 36px -8px rgba(99,102,241,0.2);\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-img-wrap {\n\t\t\tposition: absolute; top: 0; left: 0;\n\t\t\twidth: 100%; height: 100%; z-index: 1;\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-img-wrap img {\n\t\t\twidth: 100%; height: 100%;\n\t\t\tposition: absolute; top: 0; left: 0;\n\t\t\tobject-fit: cover;\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-text-wrap {\n\t\t\tposition: absolute; bottom: 0; left: 0;\n\t\t\twidth: 100%; height: 120px; z-index: 2;\n\t\t\tpadding: 12px 20px;\n\t\t\tbox-sizing: border-box; display: flex;\n\t\t\tflex-direction: column; gap: 6px;\n\t\t\toverflow: hidden; justify-content: center;\n\t\t\ttransition: height .2s ease-in-out, padding .2s ease-in-out;\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-card:hover .isw-text-wrap {\n\t\t\theight: 100% !important;\n\t\t\tpadding: 24px 20px !important;\n\t\t\tjustify-content: flex-start !important;\n\t\t\toverflow-y: auto !important;\n\t\t}\n\t\t#isw-edf3862.isw-v2 .isw-title { margin-bottom: 4px !important; margin-top: 0 !important; }\n\t\t#isw-edf3862.isw-v2 .isw-excerpt { margin: 6px 0 !important; }\n\t\t#isw-edf3862.isw-v2 .isw-readmore { text-decoration: none; display: inline-block; }\n\t\t#isw-edf3862.isw-v2 .isw-card:hover .isw-excerpt-wrap { display: block; }\n\t\t#isw-edf3862.isw-v2 .isw-card:hover .isw-readmore-wrap {\n\t\t\tdisplay: flex; justify-content: flex-end; margin-top: auto;\n\t\t}\n\n\t\t\/* \u2500\u2500 Tag \u2014 no underline, same for V1 and V2 \u2500\u2500 *\/\n\t\t#isw-edf3862 .isw-tag {\n\t\t\tfont-family: \"Figtree\", sans-serif !important;\n\t\t\tfont-size: 14px !important;\n\t\t\tfont-weight: 600 !important;\n\t\t\ttext-transform: uppercase;\n\t\t\topacity: 0.75;\n\t\t\tmargin: 0;\n\t\t\tline-height: 18px;\n\t\t\tborder-bottom: none !important;\n\t\t\tdisplay: block;\n\t\t}\n\n\t\t\/* \u2500\u2500 Title \u2500\u2500 *\/\n\t\t#isw-edf3862 .isw-title {\n\t\t\tfont-family: \"Figtree\", sans-serif !important;\n\t\t\tfont-size: 20px !important;\n\t\t\tfont-weight: 700 !important;\n\t\t\tline-height: 24px !important;\n\t\t\tcolor: #171616 !important;\n\t\t\tmargin: 0;\n\t\t\tdisplay: -webkit-box;\n\t\t\t-webkit-line-clamp: 3;\n\t\t\t-webkit-box-orient: vertical;\n\t\t\toverflow: hidden;\n\t\t}\n\t\t#isw-edf3862 .isw-title a { color: inherit !important; text-decoration: none; }\n\n\t\t\/* \u2500\u2500 Arrows \u2014 outside the slider via widget padding \u2500\u2500 *\/\n\t\t#isw-edf3862 .isw-prev,\n\t\t#isw-edf3862 .isw-next {\n\t\t\tposition: absolute;\n\t\t\ttop: calc(50% - 30px); \/* vertically centered on card area *\/\n\t\t\ttransform: translateY(-50%);\n\t\t\tbackground: none !important;\n\t\t\tborder: none !important;\n\t\t\tbox-shadow: none !important;\n\t\t\tpadding: 0;\n\t\t\tcursor: pointer;\n\t\t\twidth: 24px; height: 24px;\n\t\t\tdisplay: flex;\n\t\t\talign-items: center; justify-content: center;\n\t\t\tz-index: 10;\n\t\t\toutline: none;\n\t\t}\n\t\t#isw-edf3862 .isw-prev { left: 4px; }\n\t\t#isw-edf3862 .isw-next { right: 4px; }\n\t\t#isw-edf3862 .isw-prev::after,\n\t\t#isw-edf3862 .isw-next::after,\n\t\t#isw-edf3862 .isw-prev::before,\n\t\t#isw-edf3862 .isw-next::before { display: none !important; content: none !important; }\n\t\t@media(max-width:768px){\n\t\t\t#isw-edf3862 .isw-prev,\n\t\t\t#isw-edf3862 .isw-next { display: none !important; }\n\t\t\t#isw-edf3862 { padding: 0; }\n\t\t\t\/* V2 mobile: fixed image height, card height follows *\/\n\t\t\t#isw-edf3862.isw-v2 .isw-card { height: 500px !important; }\n\t\t\t#isw-edf3862.isw-v2 .isw-img-wrap { height: 402px !important; }\n\t\t\t#isw-edf3862.isw-v2 .isw-img-wrap img { object-fit: fill !important; height: 95% !important; }\n\t\t}\n\n\t\t\/* Dots *\/\n\t\t#isw-edf3862 .isw-dots {\n\t\t\tdisplay: flex;\n\t\t\talign-items: center;\n\t\t\tjustify-content: center;\n\t\t\tgap: 16px;\n\t\t\tmargin-top: 32px;\n\t\t\theight: 36px;\n\t\t\tbox-sizing: content-box;\n\t\t}\n\t\t#isw-edf3862 .isw-dot {\n\t\t\twidth: 10px !important;\n\t\t\theight: 10px !important;\n\t\t\tborder-radius: 50% !important;\n\t\t\tbackground: var(--e-global-color-f527530) !important;\n\t\t\topacity: 0.25;\n\t\t\tcursor: pointer;\n\t\t\tborder: none !important;\n\t\t\tpadding: 0;\n\t\t\ttransition: width .25s ease, height .25s ease, opacity .25s ease, background .25s ease, border .25s ease;\n\t\t\tflex-shrink: 0;\n\t\t\tdisplay: block;\n\t\t\tbox-sizing: border-box;\n\t\t}\n\t\t#isw-edf3862 .isw-dot.active {\n\t\t\twidth: 14px !important;\n\t\t\theight: 14px !important;\n\t\t\topacity: 1 !important;\n\t\t\tbackground: var(--e-global-color-5d532a8) !important;\n\t\t\tborder: 3px solid var(--e-global-color-f527530) !important;\n\t\t}\n\n\t\t\/* Read More *\/\n\t\t#isw-edf3862 .isw-readmore {\n\t\t\tfont-size: 14px; font-weight: 600;\n\t\t\ttext-decoration: none; color: #171616 !important;\n\t\t\tborder-bottom: 1px solid #171616;\n\t\t\tdisplay: inline-block;\n\t\t}\n\t\t<\/style><div id=\"isw-edf3862\" class=\"isw-v2\"><button class=\"isw-prev\" aria-label=\"Previous\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M15.75 19.5L8.25 12L15.75 4.5\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button class=\"isw-next\" aria-label=\"Next\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M8.25 4.5L15.75 12L8.25 19.5\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><div class=\"isw-outer\"><div class=\"isw-track\"><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/aml-transaction-monitoring\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/10\/aml-transaction-monitoring-thumbnail.webp\" alt=\"AML Transaction Monitoring and KYC: From Manual to AI\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/aml-transaction-monitoring\/\">AML Transaction Monitoring and KYC: From Manual to AI<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Traditional KYC and AML processes are struggling to keep up with modern financial crime. Learn how automation and AI are...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/aml-transaction-monitoring\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/top-fintech-app-development-companies\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/top-fintech-app-development-companies-2026-thumbnail.webp\" alt=\"Top 10 Fintech App Development Companies in 2026\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/top-fintech-app-development-companies\/\">Top 10 Fintech App Development Companies in 2026<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Looking for a reliable fintech app development partner? Explore the top fintech app development companies in 2026 and compare their...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/top-fintech-app-development-companies\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-banking-use-cases\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/AI-in-Banking-Use-Cases-Main-Blog-Thumbnail-1024x1024.webp\" alt=\"AI in Banking Use Cases: What Works and What Stalls\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-banking-use-cases\/\">AI in Banking Use Cases: What Works and What Stalls<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">A guide to AI in banking use cases, from fraud detection to document processing, the risks that stall them, and...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-banking-use-cases\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/alternative-data-in-ai-credit-underwriting\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/What-is-Alternative-Data-Main-Blog-Thumbnail-1024x1024.webp\" alt=\"What is Alternative Data in AI Credit Underwriting\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/alternative-data-in-ai-credit-underwriting\/\">What is Alternative Data in AI Credit Underwriting<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">The CFPB dropped disparate impact from ECOA in April 2026. Here is what that changed for AI credit underwriting, and...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/alternative-data-in-ai-credit-underwriting\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/banking-as-a-service\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/banking-as-a-service-thumbnail.webp\" alt=\"Banking as a Service (BaaS): How It Works\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/banking-as-a-service\/\">Banking as a Service (BaaS): How It Works<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Learn what Banking as a Service (BaaS) is, how it powers embedded finance, and how non-banks integrate accounts, cards, payments,...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/banking-as-a-service\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/synthetic-identity-fraud\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/Synthetic-Identity-Fraud-Featured-1024x1024.webp\" alt=\"Synthetic Identity Fraud Detection and Prevention\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/synthetic-identity-fraud\/\">Synthetic Identity Fraud Detection and Prevention<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Synthetic identity fraud is the fastest growing financial crime in the US. Understanding why that is and what its life...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/synthetic-identity-fraud\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/where-ai-creates-value-in-open-banking-data\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/06\/Main-5-1024x1024.webp\" alt=\"How AI creates value with open banking data\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/where-ai-creates-value-in-open-banking-data\/\">How AI creates value with open banking data<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Every fintech company with an open banking licence in Saudi Arabia must build the basic infrastructure to receive open banking...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/where-ai-creates-value-in-open-banking-data\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/navigating-1033-compliance-in-fintech\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2025\/01\/navigating-1033-compliance-thumb.jpg\" alt=\"Key strategies for navigating 1033 compliance in finance\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">Fintech<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/navigating-1033-compliance-in-fintech\/\">Key strategies for navigating 1033 compliance in finance<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">In its final rule, implementing section 1033 of the Dodd-Frank Act, the Consumer Financial Protection Bureau (CFPB) defined requirements that...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/navigating-1033-compliance-in-fintech\/\">Read More<\/a><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"isw-dots\"><\/div><\/div><script>(function(){\n\t\t\tvar id       = \"isw-edf3862\";\n\t\t\tvar spvDesk  = 3;\n\t\t\tvar spvTab   = 2;\n\t\t\tvar gap      = 20;\n\t\t\tvar total    = 8;\n\t\t\tvar autoplay = true;\n\t\t\tvar autoSpd  = 5000;\n\t\t\tvar showDots = true;\n\n\t\t\tvar wrap  = document.getElementById(id);\n\t\t\tif(!wrap) return;\n\t\t\tvar outer  = wrap.querySelector(\".isw-outer\");\n\t\t\tvar track  = wrap.querySelector(\".isw-track\");\n\t\t\tvar slides = wrap.querySelectorAll(\".isw-slide\");\n\t\t\tvar dotsWrap = wrap.querySelector(\".isw-dots\");\n\t\t\tvar btnPrev  = wrap.querySelector(\".isw-prev\");\n\t\t\tvar btnNext  = wrap.querySelector(\".isw-next\");\n\n\t\t\tvar cur      = 0;\n\t\t\tvar timer    = null;\n\t\t\tvar spv      = spvDesk;\n\n\t\t\tfunction getSpv(){\n\t\t\t\tvar w = window.innerWidth;\n\t\t\t\tif(w <= 480)  return 1;\n\t\t\t\tif(w <= 900)  return spvTab;\n\t\t\t\treturn spvDesk;\n\t\t\t}\n\n\t\t\tfunction setSlideWidths(){\n\t\t\t\tspv = getSpv();\n\t\t\t\tvar outerW = outer.offsetWidth;\n\t\t\t\tvar slideW = (outerW - gap * (spv - 1)) \/ spv;\n\t\t\t\t[].forEach.call(slides, function(s){\n\t\t\t\t\ts.style.width    = slideW + \"px\";\n\t\t\t\t\ts.style.minWidth = slideW + \"px\";\n\t\t\t\t});\n\t\t\t}\n\n\t\t\tfunction maxPage(){ return Math.max(0, total - spv); }\n\n\t\t\tfunction goTo(n, instant){\n\t\t\t\tn = Math.max(0, Math.min(n, maxPage()));\n\t\t\t\tcur = n;\n\t\t\t\tsetSlideWidths();\n\t\t\t\tvar slideW = slides[0] ? 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touch.<\/p>\n","protected":false},"author":76,"featured_media":219189,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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