Predictive Analytics in BFSI Domain

The market is watching an unprecedented shift in Business Intelligence (BI), widely because of technological innovation and expanding business requirements. The recent shift in the BI market is the move from traditional analytics to predictive analytics. Needless to say that predictive analytics is a part of BI family; however, these days that it is emerging as a distinct new software sector.

Though traditional analytical tools claim to give a real 360° view of the business, but they evaluate only historical data. Traditional analytics can help gaining insight for what was right and what went wrong in decision-making. However, these tools provide just a rear view analysis. One cannot reverse the past, but one can surely prepare better for the future and that’s what decision makers across the globe want to know the future, how to regulate it and take necessary actions to attain goals.

Innovators across BFSI sector have also started using big data and analytics to sharpen risk assessment and drive

The BFSI industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. As banks face increasing pressure to stay profitable, understanding customer needs and preferences becomes a critical success factor. New models of proactive risk management are being increasingly adopted by major banks and financial institutions. Through Data mining and advanced analytics techniques, banks are better equipped to manage market uncertainty, minimize fraud, and control exposure risk.

According to IBM’s 2010 Global Chief Executive Officer Study, 89 percent of banking and financial markets CEOs say their top priority is to better understand, predict and give customers what they want. Financial metrics and KPIs provide effective measures for summarizing your overall bank performance.

As per Deloitte research, the main three business drivers, which increase the importance of analytics within the banking industry, are:

Customer Analytics

Banks and credit unions are consistently at risk of losing customers and in order to ease the flow, they may offer their best customers better rates, waive annual fees and prioritize treatments. However, such retention strategies have related costs, and one cannot afford to make such offers to every single customer. The success and feasibility of such strategies is dependent on identifying the right action for the right customer. Smarter banks have started increasingly invest in customer analytics to gain insights into new customers and effectively segment their clients. This significantly helps them determining pricing, new products and services.

Risk Analytics

IT systems transformed virtually every single bank process in the 1980s and 1990s. Today, banks have that rare opportunity to reinvent themselves again—with data and analytics. Banks and Financial Services are investing in risk analytics and intend to increase those investments, yet the potential return is often stifled by inconsistent or incomplete data. This prevents organizations from generating the insights needed to support a more predictive approach to risk management.

Bank industry is predicting the greatest increase in risk analytics investments, with 73 percent of banking respondents foreseeing more than a 10 percent rise in expenditure. In terms of specific capabilities, risk analytics spending is expected to increase most in areas of data quality and sourcing, systems integration and modeling.

Fraud Analysis

Fraud detection in banking is a detracting activity that can span a series of fraud schemes and fraudulent activity from bank employees and customers alike. Since banking is an extremely regulated industry, there are also many external compliance requirements that banks must adhere to in the combat against fraudulent and criminal activity.


Predicting future business outcomes, once thought to be a luxury known only to data scientists, is now becoming more ubiquitous—available to business users and knowledge workers across all functions of an organization, like sales, marketing, operations, customer service and BFSI.

Today’s predictive analytics technology allows organizations and empowers business users of all skill levels to make sense of their business data, improve forecasts, and gain a better understanding of their customers, as well as identify and respond to new opportunities faster.

Implementing a unified analytics platform that incorporates and aggregates all of the metrics financial firms rely on to run their businesses reduces computational complexity across instruments and data silos. It facilitates dynamic monitoring of positions, markets, events and capital flows.

This article was written by Rajdeep Dutta, Head of Redwood Knowledge Centre.