The unprecedented growth in the data and digital technologies like Artificial Intelligence (AI), Robotics Process Automation (RPA), Blockchain, Machine Learning (ML) have aptly led to the evolution of Industrial Revolution 4.0. Never before in the history of business has machines been able to almost match human intelligence with such ease. Undoubtedly, ML is disrupting almost all functions within a business even though at a different rate. Its impact on the Banking, Financial Services and Insurance (BFSI) domain is unmatched because ML-based algorithms predict more accurate results when fed with more data. Coincidentally, the BFSI sector provides for petabytes of data on the customer, transactions, invoices, money transfers, etc. to help these algorithm-based models learn and improve continuously.
The banking industry in India is forecasted to spend over USD 2 Billion on ML based products and services. Beside some rudimentary applications of ML in banking like designing voice assisted banking processes or customizing customer offering, applications embedded in end-user devices, personal robots, and financial institution servers are capable of analyzing massive volumes of information, providing customized financial advice, calculations and forecasts. These applications help in developing financial plans and strategies, and track their progress.
ML is also useful in wealth management for clients to analyzea large amount of unstructured data for detecting customer’s behavioral patterns. By automating a large part of the process, banks offer personalized, tax-optimized investments to clients.
An unmatched solution offered by ML in the banking domain is a faster, more accurate, and cost-effective decision-making system for credit scoring and lending. As compared to the traditional credit scoring model based on pre-defined rules, ML provides a more complex and sophisticated system to help bankers distinguish between high-level risk applicants from more credit-worthy ones.
ML techniques are also being used by regulated institutions for regulatory compliance and by authorities for supervision. SupTech is the use of ML-enabled technologies by these public sector regulatory institutions to develop applications that enhance efficiency and effectiveness of supervision and surveillance.These ML-based techniques are also used by financial services to detect threats such as lending frauds, ATM hacks, money-laundering, and cyber-attacks. Some of these techniques are also used to develop models that help determine hidden patterns in unstructured data sets that are impossible to track using common statistical procedures.
In the insurance sector, consumers have come to expect personalized solutions, and ML helps review customer profiles, and based on it, offers specific insurance products suitable to the needs of the customer. Another application of ML in the insurance sector is while processing thousands of claims and responding to several customer queries daily. AI offers solutions that help improve this process by streamlining the movement of these claim-related documents from initial application to a final decision in almost no time.
In the entire BFSI sector, ML models assist in identifying and highlighting fraudulent claims for further human investigation and help ensure an objective decision-making process to avoid human bias. Not only this, these companies use ML-based solutions to monitor activity levels of customers and reward good behavior of the customer by offering them discounts or appropriate type of policies. With such a holistic customer view, insurance companies can better manage risk too.
Companies have already started to witnessthe benefits of ML in financial services as these are multiple and hard to ignore. Forbes surveyed senior financial managers, 65% of whom expect positive changes emerging out of the use of ML in financial services. However, only thirty percent of the companies have taken a plunge to implement ML-based solutions into their company processes as of date. Several of these companies are more concerned about the time, effort and expense associated with implementing these solutions in financial services versus the expected ROI.
Now, the challenge to be addressed is that one can’t simply deny the importance of this technological progress and delaying its adoption today may cost heavily to firms in the long run.