How Machine Learning Holds The Potential To Revolutionise The BFSI Sector

Data dominates today’s financial world, and ever-evolving technologies such as machine learning are its able drivers. Technology has brought a notable transition in the way BFSI sector operates today. It has transformed traditional lending practices and processes, unlocking newer and better ways to gauge borrower creditworthiness by leveraging intelligent algorithms.

Since banks deal with a deluge of consumer data, a well-trained machine learning system can run through thousands of customer profiles and build analytical models. It can perform diverse underwriting and credit-scoring tasks in real-time, thus introducing greater speed and accuracy into these processes.

For instance, lenders can leverage machine learning algorithm to study prospective borrowers’ bill payment behaviour via open APIs and come out with accurate credit scores for them for approval.

Differentiation Machine Learning Can Create

Processes such as verifying and extracting essential details from documents are now automatically done using NLP (natural language processing). While doing it manually, even for one borrower, would have taken a lot of time, machine learning performs the same task for multiple borrowers almost instantly. This helps lenders process loan applications faster and approve a larger set of borrowers within minutes.

With machine learning, banks can also digitally advise customers and help them make the right investment decisions. Financial advisors or relationship managers are being replaced by digital advisors, which are essentially ML algorithms that keep getting better and better with time and data.

Intelligent algorithms analyse historical data and offer predictions to maximize capital gains while minimizing risks. These algorithms understand the investor’s financial goals and risk capability before presenting the right and relevant investment options.

And that’s not all. Machine learning also has the potential to curb credit frauds, which continues to be the BFSI sector’s one of the major challenges. ML systems detect these frauds by evaluating multiple transactions in real-time, basis certain predefined parameters; this includes mapping every single action taken by a cardholder and highlighting suspicious transactional behaviour using algorithmic calculations.

ML systems can also flag potential cyber fraud or unauthorised access by analysing usage patterns, nature of transactions, location of the query, number of attempts, number of invalid data entries, etc. Banks can immediately notify the user about suspicious activities and block the transaction if need be.

Machine learning can also improve regulatory compliance by learning and remembering the relevant laws and ensuring they are followed to the letter, thus minimizing the risk of human error.

In Conclusion

Given the differentiation that they can deliver, banks and NBFCs are heavily investing in machine learning tools to ensure efficient credit profiling and improving their end-customer offerings. Several players in the BFSI domain are already leveraging machine learning to offer best-in-class financial services to customers.

The technology holds significant potential to revolutionise the financial services sector. The difference between surviving and thriving in the BFSI domain in the future could ultimately boil down to those who eagerly embrace evolving technology and those who fail to adapt themselves for this new-age paradigm.