Written by: Sarita Digumarti, Co-founder & COO, Jigsaw & Chief Learning Officer at UNext Learning
One thing we as humankind were sure of was the fact that the future couldn’t be predicted. It has always been definite and real but never certain. Until, revolutionary tech concepts like Big Data, analytics, Artificial Intelligence (AI) and machine learning became mainstream.
Today, most of us are aware that with an adequate volume of quality data, we can try to use trends in historical data to make reasonable predictions about the future. This is what predictive analytics is all about and despite being a concept dating back to the 1940s, its implementations have garnered significant momentum fairly recently thanks to the boom in our ability to not only generate but store and process colossal volumes of data.
With major improvements in predicting outcomes and scenarios, there’s one industry that is making the most of predictive analytics for a variety of purposes – the Banking, Financial Services and Insurance sector (BFSI).
As more ways to transact money and money’s worth and banking continue to pop up, BFSI companies are finding it hard to keep up with volatile shifts in customer demands, adapt to new workflows and tackle newer security challenges. Predictive analytics is a key strategic bankable solution the BFSI industry is able to leverage.
Projected to be a $28.1bn industry by 2026, the predictive analytics market is fast changing conventions in the ways banking and financial institutions operate. With over 52% of the companies using predictive analytics models for their operations and purposes, let’s look at some of the most eminent ways predictive analytics is solving diverse real-world problems in BFSI space.
Fight Financial Frauds
Currency is not the only way to transact money and money’s worth today and physical banks are not the only places to manage our finances and accounts. Today, there are digital payment systems, wallets, cryptocurrencies, mobile banking and more avenues for us to utilize. With so many options, what has simultaneously increased apart from customer satisfaction and ease of banking is the number of financial frauds and counterfeit transactions.
To bring some perspectives, let’s note that India reported 229 banking frauds (valuing up to 1.38 lac crore) every day between 2020 and 2021. The more alarming insight is that less than 1% of the amount has been recovered as yet.
To tackle this, financial institutions are increasingly relying on advanced algorithms powered by machine learning and predictive analytics to accurately predict instances of fraudulent transactions and identity thefts, improve KYC automations, detect unusual patterns in individual usage and more. Predictive analytics, in this aspect, is helping companies mitigate crimes and concerns before they occur.
From a financial institution perspective, there is always a significant amount of risk involved whenever a loan is disbursed. For a bank, therefore, accurate assessment of risk of default is critical. To make the process of loan approvals as well as collections more airtight, banks are leveraging predictive analytics models. This helps them gauge the repayment ability of applicants, understand patterns in previous loans and more.
Besides, such models also shatter conventional ways of approving loans by coming up with newer parameters for disbursals which would get otherwise rejected in legacy ecosystems.
Insurance companies, too, are making use of such models to come up with customized insurance plans and premium amounts based on individual profiling and assessments such as driving records, medical history, credit scores, weightage of documents submitted and more.
Better Customer Management
Cluttering market conditions is something that every business across every industry is facing currently and BFSI is no exception. With so many conventional financial institutions existing and new-age unicorns springing up, organizations must take very precise approaches to retain their existing customers and acquire new ones.
With abundant data at disposal, companies are now utilizing predictive analytics models and algorithms to revisit their engagement strategies:
Newer loyalty programs and incentives are replacing conventional ones
Patterns are studied on customer churns
Precise recommendation engines are being tested to exactly understand what customers require at specific timings and more to deliver value in maintaining relationships
Cross-selling is being done by organizations to better retain and engage their customers
Even before a customer is onboard, institutions are making efforts to sharpen their targeting through predictive analytics and conveniently find customers who are looking for them and more
This is making BFSI companies more holistic and end-to-end in terms of their operations and positioning.
We have just scratched the surface of predictive analytics’ potential and its application in the BFSI sector. As we increase the number of data touch points, develop advanced models and proceed further with developing narrow AI, we could look at more exciting and reassuring ways financial institutions could offer banking services to customers.