TURNING DATA INTO TRANSFORMATIVE
INSIGHTS
Data analytics in banking leverages data to personalize customer experiences, detect fraud, improve risk management, optimize processes, and monitor operations in real time. Banks often invest in specialized analytics platforms, artificial intelligence (AI) tools, and robust data infrastructure to ensure efficient integration and analysis of various data sources.
Facts about data analytics in banking
Market growth
The banking data analytics market is growing at a 23% compound annual rate, increasing from $8.6 million in 2024 to $24.3 million in 2029.
Implementing costs
Banks invest between $100,000 to $1.5 million in data analytics, depending on project scope and tools chosen.
Cost savings
Banks leveraging data analytics see an average 5% increase in earnings-per-share.
Data analytics in banking: Key features
Data analytics in banking can be broken down into specialized areas, each serving a specific purpose in improving operational efficiency, customer experience, and regulatory compliance. Here are the key features within each area:
Financial analytics
- Profitability analysis: Examining the revenue and costs of different products, services, and customer segments to improve financial performance.
- Liquidity management: Monitoring cash flow to ensure adequate liquidity and capital reserves.
- Investment analysis: Supporting investment decision-making by analyzing market trends, asset performance, and economic indicators.
- Financial forecasting and budgeting: Projecting future financial scenarios based on historical data and predictive models, aiding in strategic planning.
Performance analytics
- Branch and channel performance: Monitoring the effectiveness of branches, ATMs, online banking, and other banking channels, to optimize resource allocation.
- Employee productivity: Assessing individual and team performance metrics to track productivity, set realistic targets, and reward high performers.
- Financial KPIs: Measuring key financial indicators such as revenue growth, profitability, and cost-efficiency to meet strategic goals.
- Benchmarking and trend analysis: Comparing performance metrics against industry standards and historical data to identify areas for improvement.
Customer analytics
- Customer segmentation: Utilizing demographic, geographic, and behavioral data to segment customers for targeted service offerings.
- Customer lifetime value: Calculating customers’ potential value over their relationship duration to prioritize high-value clients.
- Personalization and recommendations: Analyzing individual preferences and behaviors to suggest tailored products.
- Churn prediction: Identifying patterns of customer dissatisfaction or likelihood of leaving, enabling proactive retention strategies.
Marketing analytics
- Targeted campaigns: Identifying optimal customer segments for marketing campaigns to improve conversion rates and reduce acquisition costs.
- Customer journey mapping: Tracking customer interactions across channels to refine marketing strategies and create better customer experiences.
- Campaign performance measurement: Evaluating marketing campaign success by tracking metrics such as return on investment, click-through rates, and customer engagement.
- Cross-sell and upsell opportunities: Identifying customers who may be interested in additional products to increase revenue per customer.
Operational analytics
- Process optimization: Analyzing workflows in areas like loan processing, customer service, and transaction handling to reduce bottlenecks and improve efficiency.
- Resource allocation: Assisting in optimizing staffing and resource allocation based on usage patterns to boost productivity and customer service.
- Predictive maintenance: Anticipating maintenance needs for physical infrastructure like ATMs to reduce downtime.
- Cost reduction: Identifying cost-saving opportunities through process automation, resource optimization, and efficiency improvements.
Reporting analytics
- Automated report generation: Creating standardized reports for internal and external stakeholders to ensure timely and accurate delivery of information.
- Customizable dashboards: Providing management with interactive dashboards that display real-time insights into key performance metrics.
- Data visualization: Representing data in charts, graphs, and other visual formats to make complex data accessible to non-technical stakeholders.
- Historical and predictive reporting: Offering insights into past performance and predicting future outcomes to support strategic planning and decision-making.
Compliance analytics
- Anti-money laundering monitoring: Analyzing transaction data to detect suspicious patterns that could uncover money laundering activities.
- Know your customer compliance: Verifying customer identities and assessing risk profiles to comply with regulatory requirements and reduce fraud risk.
- Fraud detection and prevention: Analyzing deviations from typical transaction patterns in real time to identify fraudulent activities.
- Regulatory automation: Automating data collection and reporting for regulatory submissions to improve accuracy and reduce compliance costs.
Risk analytics
- Credit risk assessment: Utilizing customer data, credit scores, and behavioral indicators to assess loan eligibility and set credit limits.
- Market risk management: Monitoring market variables, such as interest rates and currency fluctuations, that could impact the bank’s financial position.
- Operational risk analysis: Identifying potential risks in processes, systems, or human errors to help banks mitigate operational risks.
- Compliance and regulatory risk: Evaluating the bank’s exposure to regulatory changes and adapting to new compliance requirements.
Each of these areas contributes to a financial organization’s data-driven approach, enabling better decision-making, streamlined operations, enhanced customer satisfaction, and stronger compliance. Together, they help banks leverage their data to gain competitive advantages in the market.
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Data analytics in banking: Use cases
Data analytics in banking plays a crucial role across various applications. Below are some of the primary uses cases:
Customer segmentation and personalization
- Banks can understand customer behavior, preferences, and needs by segmenting and analyzing data.
- Customers get personalized services and targeted marketing, which enhances their engagement and loyalty.
- Financial institutions can use next-best-action strategies to offer relevant products based on customer history
Fraud detection and prevention
- Banks use analytics to reduce fraud losses by analyzing transaction patterns and flagging anomalies in real time.
- With advanced machine learning models, financial institutions can improve accuracy further by learning from historical fraud cases.
Credit risk assessment
- Analytical tools help banks assess and monitor credit risk by analyzing a range of data, like credit history and financial behavior, making risk scoring more accurate and reducing defaults.
- Some models integrate alternative data sources, such as social media and industry trends, to provide a more comprehensive risk profile.
Operational efficiency
- Financial institutions can identify inefficiencies and optimize workflows by analyzing internal processes and performance metrics.
- Analytics-driven insights can lead to process improvements, resource savings, and productivity increases.
Compliance and regulatory reporting
- Using data analytics, banks can automate the collection, analysis, and reporting of compliance data.
- Financial institutions can ensure compliance with evolving regulations by generating accurate reports and reducing errors.
Product development and market analysis
- Banks use data analytics to analyze market trends, identify customer needs, and develop new products.
- With market and consumer behavior data, they can adjust strategies to meet changing demands, giving them a competitive edge.
With data analytics, financial institutions can operate more efficiently and remain competitive in a fast-evolving digital landscape, reducing operational costs, improving customer satisfaction, and managing risk.
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Data analytics in banking: Integrations
Many banks integrate data analytics tools and platforms with internal and external systems to maximize data utility and streamline operations. Here are some key integrations:
Core banking systems (CBS)
Integrating analytics with core banking systems enables real-time data flow between different banking functions. This integration allows financial institutions to analyze transaction patterns, manage risk, and improve operational efficiency.
Customer relationship management (CRM) systems
Banks integrate analytics tools with CRM systems to comprehensively understand customer interactions. This makes it easier to personalize services, segment customers, and target marketing, improving customer retention and satisfaction.
Risk management and compliance systems
Banks employ data analytics to monitor transaction data, ensure customer due diligence, and automate reporting processes to meet regulatory requirements. These integrations make complying with anti-money laundering (AML) and other regulations easier.
Digital banking applications
Financial institutions leverage analytics with mobile and online banking platforms to deliver tailored customer experiences. By analyzing customer behavior on digital channels, banks can personalize user experiences, offer customized products, and increase engagement.
Accounting and treasury systems
Banks integrate analytics with accounting and treasury platforms to detect errors, report financials, and manage cash flow. A bank can track and analyze its financial position more accurately, which makes budgeting, forecasting, and portfolio management easier.
External data sources
Financial institutions use external data sources like credit bureaus, social media, and data marketplaces to enrich insights. As a result, they can make better decisions based on credit risk, market trend, and customer sentiment analysis.
These integrations can result in banks creating a unified data ecosystem, enabling real-time insights, proactive decision-making, and enhanced customer service.
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Implementing costs of data analytics in banking
| Costs | Solution |
| $100K-$300K |
|
| $300K-$600K |
|
| $600K-$1.5M+ |
|
Source: ScienceSoft Finance
Costs:
$100K-$300K
Solution:
- Tracks KPIs across 1–2 analytics areas, such as financial and marketing metrics.
- Connects to 1–2 key data sources, such as a core banking system.
- Allows batch data processing, such as every 12 hours.
- Maintains scheduled and ad hoc reporting.
Costs:
$300K-$600K
Solution:
- Tracks KPIs across multiple business areas, including finance, employee performance, and customer service.
- Connects with 3–7 key data sources.
- Provides batch as well as real-time data processing.
- Provides diagnostic and predictive analytics using non-neural networks.
- Automates regulatory reporting.
Costs:
$600K-$1.5M+
Solution:
- Monitors metrics, including market performance.
- Connects with multiple back-office systems and third-party software, including blockchain-based financial technology.
- Enables instant KPI calculation and event monitoring, such as financial fraud detection.
- Utilizes machine learning to provide advanced root cause analysis and forecasting.
- Optimizes and personalizes based on AI.
- Automates the generation of complex reports, including consolidated reports and reports that comply with local regulations.
- Supports customized reporting following established reporting forms.
Source: Science Soft Finance
Data analytics in banking: Benefits
Data analytics offers a number of benefits for organizations in a range of industries, including banking. According to a Forrester survey, banking and financial services organizations achieved the following results:
96%
reported intermediate or advanced maturity in data analytics
79%
had upticks in revenue performance from leveraging data analytics
47%
identified investing in better data analytics capabilities as a top investment priority
Banking and financial services industry have seen the greatest benefits and cost savings from data analytics of any industry surveyed.
Data analytics consulting services
Business intelligence (BI)
- 10Pearls’ consultants implement processes so you can see key performance indicators (KPIs) in real time across your financial departments.
- We use AI-powered BI tools to help you process and analyze financial data.
Data strategy
- We help you accelerate data transformation without disrupting your operations.
- Based on your strategic goals, we develop a roadmap that maximizes ROI for various data use cases.
Security and compliance
- Our privacy frameworks protect sensitive data and ensure data access.
- We help you comply with data security, privacy, and financial regulations.
AI and data science
Predictive analytics
- We analyze historical financial data using AI models to identify patterns, which allows us to develop predictive models.
Natural language processing (NLP)
- We can analyze unstructured data sources using AI-powered NLP tools,
- We help you discover valuable financial data to help you make informed decisions.
Recommendation engine
- Using AI, we assess user behavior and preferences and provide product and content recommendations, depending on the application.
Let 10Pearls help you achieve data analytics in banking benefits
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Data engineering
Data migration
- We migrate financial data from legacy warehouses to modern databases with minimal downtime and improved operational speed.
- By migrating legacy systems incrementally, our database experts guarantee business continuity.
Data modernization
- Our consultants work with you to modernize legacy platforms to make data migration as painless as possible.
- Financial institutions and fintech companies can become innovative and agile by reducing their dependency on legacy systems.
Data augmentation
- We help you get the most out of your machine-learning models.
- With our data augmentation methodology, you can expand your dataset, improve its quality, and boost model accuracy.
Data transformation
- Using industry-standard tools, we help you extract, transform, and load (ETL) data from various sources.
Analytics platform implementation
Cloud warehousing solutions
- We build cloud data warehouses for financial reporting and analytics.
- We move your legacy systems to cloud platforms that are cost effective and intuitive.
Data lake implementation
- We quickly establish cloud data lakes to find, process, and assess structured and unstructured data.
- Data streams are ingested in real time, and our auto-tiered storage keeps costs low.
Connected data architectures
- We combine your analytics databases, tools, and environments into a cohesive data fabric.
Reporting & analytics
- We use cutting-edge tools to display raw data in an interactive and intuitive way.
- We create dashboards and reports to ensure your data analytics has real-world impact.
Data analytics in banking case studies
Fintech
Innovating identity protection
Sontiq, an intelligent identity security startup, needed a flexible partner to support the architecture and delivery of their identity protection platform without the overhead of a full-time team. 10Pearls provided a skilled team to develop a scalable, high-impact solution.
fintech
Accelerating credit union innovation
Driving digital transformation within the credit union industry by giving credit unions access to a marketplace of financial and fintech services through an open development platform.
fintech
Customizing core banking solutions
A fintech company specializing in digital banking customization was facing substantial drop-offs due to inefficient service functionality. 10Pearls developed an agile digital banking solution equipped with mobile-optimized features to seamlessly integrate with core banking, third-party, and risk mitigation services.
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10Pearls is an award-winning data analytics in banking consulting and software development company that helps financial institutions and fintech companies with product design, development, and technology acceleration.