How Banks Use Data Products to Provide Deeper Insights and Improve Decision-Making

Just around three yrs ago, I had the privilege of working in the IT department of a well-known bank in the UK, and it was fascinating to witness firsthand how digital technology is a key driver for the bank's overall business. IT is no longer just limited to support activities, but rather, it is a crucial enabler for the growth of the core banking business.

One of the most exciting aspects of my working in the data and performance engineering team was the opportunity to gain exposure to various IT products across different areas. While the end-user experience is undoubtedly critical, it's essential to recognize that much of the work that goes on behind the scenes involves building enterprise data models and developing data-driven products and insights.

To ensure a seamless customer journey, these products and insights are designed to enable quicker and better decision-making through a data-driven user interface. Continuously fine tuning of each of front-end, middleware, and data model is an ongoing activity for any forward-thinking bank , Recently with the prominence of AI and ML, these technologies are providing intelligent solutions that make life easier for both bankers and customers. As a result, the banking industry is constantly evolving, and it's fascinating to see how technology is driving this change.

today let me share few data products that are frequent used in bank in one or other way , common data products are as below

Credit reports: Banks often offer credit reports which include a detailed summary of an individual’s credit history, including their credit score, outstanding debts, and payment history.

Account statements: Banks provide their customers with monthly or quarterly account statements that list all transactions made during a particular period, along with the current balance.

Fraud monitoring services: Banks provide services that monitor customer accounts for suspicious activity and notify them if any unusual transactions are detected.

Analytics and insights: Banks offer various analytical services to help customers understand their spending habits, cash flow, and investment opportunities.

Loan data: Banks maintain data on loan applications, approvals, and repayments, which can be used to develop credit models and risk assessments.

Investment data: Banks offer investment data, including real-time stock prices, news, and analysis, to help customers make informed investment decisions.

These are just a few examples of the data products that banks offer. However, the specific products offered can vary depending on the bank and the needs of its customers.

In addition to the data products mentioned earlier, banks may also offer insights-based data products. These data products leverage advanced analytics and machine learning algorithms to provide deeper insights into customer behavior and preferences and to help banks make more informed decisions.

Some examples of insights-based data products that banks may offer include:

Predictive analytics: Banks may use predictive analytics to forecast customer behavior and identify potential risks or opportunities. This can help banks make more informed decisions about product development, marketing campaigns, and risk management.

Customer segmentation: Banks may use customer segmentation to group customers based on their behavior and preferences. This can help banks develop targeted marketing campaigns and personalized product offerings.

Fraud detection: Banks may use machine learning algorithms to detect and prevent fraud in real-time. This can help banks minimize losses and protect customer data.

Risk management: Banks may use risk management tools to identify potential risks and develop strategies to mitigate them. This can help banks protect their financial assets and ensure regulatory compliance.

Overall, insights-based data products can help banks gain a competitive edge by providing deeper insights into customer behavior and preferences and by enabling more informed decision-making. However, building these products requires the involvement of multiple stakeholders, including functional teams, data scientists, and data and ML engineers, to collaboratively and iteratively build a mature insights data product for better customer experience and contributing to the growth of the bank.