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Data analytics has already become the backbone of business decision-making in the banking sector, and its importance only increases as the volume of data grows. The use of data and advanced analytics were the top priorities for banking executives in 2024, as revealed by the 2024 Retail Banking Trends and Priorities report.
In this context, banks need solutions for financial data analytics to transform raw operational, financial, and customer data into meaningful and actionable insights. A bank can use these insights to enhance its performance, improve financial health, provide better financial advice, and prevent fraud.
This article highlights four essential financial data analytics capabilities for a banking institution.
Automated credit scoring
A traditional manual credit underwriting process can be error-prone, as even an experienced bank credit analyst can forget to request some information about a borrower from a credit bureau or make a mistake during information assessment. Such errors can lead to wrong lending decisions that can negatively affect a bank’s profitability, which is why improving the accuracy of credit underwriting is critical. A robust financial data analytics software can help a bank assess borrowers’ credibility more precisely and efficiently.
After a borrower submits a loan application, the software can automatically calculate his or her credit score based on the analysis of provided personal and financial info (income history, employment history, liabilities, etc.). In case a borrower has previously taken out loans, the software can also analyze data from credit agencies’ databases (if there’s API integration in place) to run a more comprehensive credit assessment. The score generated by the solution can help a bank credit analyst decide whether to approve the loan and, if so, on what terms.
Cash flow forecasting
Cash flow analysis is essential for banks to maintain financial health and make business more attractive to investors. However, as banking operations increase and become more complex, running a cash flow analysis becomes challenging, and this is where financial data analytics software can come in handy. Such a solution can provide an in-depth assessment of a bank’s entire cash flow or some of its parts (credit, accounts payable, accounts receivable, etc.) over a specific time period.
Using AI-powered data analytics models, banks can get even more advanced cash flow forecasting and predict their future cash balance based on historical financial data related to sales, cash inflows, and cash outflows. This way, a bank can identify potential problems on the operating, financing, and investing levels and solve them in advance, as well as plan finances more efficiently.
Portfolio performance forecasting
Investment banks primarily act as intermediaries between companies in large deals, but they can also work as advisors, guiding clients on how and where to invest. In this context, the ability to analyze and evaluate their clients’ portfolios and identify potential risks accurately is critical to the success of an investment bank.
A robust data analytics tool equipped with forecasting functionality allows the investment bank to predict the performance of a client’s portfolio by anticipating its absolute, annual, or risk-adjusted return and provide financial advice based on this information. The provided financial recommendations can help a bank’s customers make more intelligent investment decisions and achieve desired financial goals, which in turn can increase customer satisfaction and loyalty.
Fraud detection and prevention
As financial fraud becomes more sophisticated and widespread across the globe, its cost continues to increase. In 2023, almost 60% of banks, credit unions, and fintech companies lost over $500,000 due to direct fraud, according to the 2024 State of Fraud Benchmark Report. A robust financial data analytics software can help a bank detect and prevent fraud more efficiently.
For example, a bank can implement an AI-powered data analytics tool to monitor financial transactions and identify abnormal or non-standard ones, since they may indicate real-time payment fraud. In addition, an institution can use a data analytics solution to examine its clients’ financial documents and income statements and identify malefactors using the bank for money laundering.
Final thoughts
Financial data analytics software is an indispensable tool for any modern financial institution, and banks are no exception. These software solutions enable both retail and investment banks to examine raw financial and customer data and get valuable business insights based on the analysis. Automated credit scoring, fraud detection, cash flow forecasting, and portfolio performance forecasting are some significant capabilities these powerful tools can provide.
However, implementing such complex tools as financial data analytics software can be challenging. A bank needs to not only select the right solution but also integrate it with multiple data sources and run necessary customizations to implement it properly, which requires technical expertise. However, a bank doesn’t have to execute data analytics implementation by itself and can instead outsource software developers to handle both business and technical aspects of this process and ensure smooth adoption.
Reviewed and edited by Albert Fang.
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Article Title: 4 Must-have Capabilities of Banking Data Analytics Tools
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