Forbes revealed that BFSI is the top industry leveraging big data analytics but there seems to be a long way to go. Several examples of how financial institutions have used advanced analytics to solve business challenges are testimony to how analytics will continue to impact BFSI. According to McKinsey, a European bank used machine learning algorithms to predict the customers who are more likely to sever ties with the bank and reduce business. A targeted campaign followed that reduced this percentage of customers by 15%. Another US bank offering discounts to customers through their private bankers used business analytics to uncover a pattern of unnecessary discounts raising revenue by 8% in a few months. All this means that BFSI has embraced business analytics to find answers to their challenges and survive amidst stiff competition. On the other hand, professionals in this domain will need to upskill to stay relevant in their jobs and leverage this trend for higher salaries and career growth.
There are three key areas highlighted by Genpact where analytics has created the maximum impact in BFSI:
- Risk, Fraud, and AML (Anti-Money Laundering)/KYC (Know Your Customer) Analytics – Financial institutions can analyze customer portfolios for assets and liabilities to identify defaulters and forecast losses to plan solutions. Banking professionals also help risk mitigation faster and reduce complex process expenses especially on Know-Your-Customer (KYC) and AML (Anti-Money Laundering) divisions. Fighting fraud with analytics have a positive domino effect as it improves profitability, reduces long withstanding legal hassles and payouts, and improves customer satisfaction. Professionals can focus on real threats effectively by reducing false alerts rather than the conventional audit cycles. Advanced analytics identifies patterns of fraudulent transactions, predict the next fraud in progress, and notify both the bank and customers saving responsive measures later on. Anti-money laundering incidents are on a high; but with analytics, non-compliance fines can be easily implemented and reputation loss risk mitigated.
- Product and Portfolio Optimization Modelling – Asset pool quality, defaults, cash-flow, delinquencies can all be determined using advanced customer portfolio analytics techniques. Predictive and prescriptive analysis can be used for calculating portfolio risk measures (example: Value at Risk) and simulations to predict defaulters based on lagging and leading indicators. Stress testing of customized scenarios can be performed and adjustments to LTV (Loan-to-Value) ratios according to regulations can be made.
- Consumer Behavior and Marketing Analytics – Bankers had never been able to study and understand their customer and prospect base, as well as, they do with advanced analytics. By leveraging analytics, they have been able to improve their marketing outcomes substantially. They are able to leverage digital channels and reduce the go-to-market time without a considerable dent in their marketing budgets. More profitable customers can easily be identified along with opportunities to cross-sell and up-sell. It also helps with migrating customers from less profitable relationships to more profitable ones. Highly-targeted marketing campaigns and new product offerings lead to the acquisition of more profitable customers.
BFSI professionals would benefit most from learning business analytics as it provides opportunities to apply descriptive, predictive, and prescriptive analysis to typical banking challenges and develop a strategic mindset to manage multiple competing priorities. The BFSI domain is looking to enhance their capabilities with the right talent and it is the right time for professionals to upskill and learn business analytics to stay relevant and meet their employer expectations. Take cue from Great Lakes alumnus Mandvi Tyagi who made a successful career as a business analyst with RBS by learning and using analytics.