Strategies for proactive prevention of loan delinquencies by Rural Indian Customer using collection and analysis of Alternate data

Abstract

The Financial Service sector of our country consists of mainly Commercial and Cooperative Banks, capital markets, insurance, Non-Banking Finance Companies (NBFCs),etc. Post subprime crisis, our country s financial system has significantly changed with many regulatory reforms, emerging technology solutions, etc. newlineOver a period of time, the NBFCs in India have grown and expanded drastically in geography and diverse category of operations. The business model, customer profile and nature of financial products vary substantially depending on the category of the NBFC. The sector has been growing shoulder to shoulder with Private banks in terms of size and in fact has been a step ahead in terms of modern banking solutions, use of technology and providing services to the Customers. The Sector has seen many ups and downs during this time period. Many challenges have come across and one of the biggest challenges it continues to face is the problem of loan delinquencies and loan defaults. Majority of the NBFC s in the country have their business offices set in rural areas of the country due to the business opportunities lying in those areas. It helps the country in achieving inclusive growth but at the same time the NBFC s face a major problem of rural loan defaults. There are many traditional methods that the banking sector and Non banking sector uses for the Loan assessment before approving the loan. But post disbursement, there are very limited methods and factors that the creditors consider for the re rating of the customer and possible prediction of Loan default. newlineThe modern times, with the help of technology and modern lifestyle of the borrowers, has given rise to many new methods and factors which can help in predicting the Loan defaults and possibly reducing them as well. Alternate data factors can be said to be one of such factors which can be useful in prediction of possible loan delinquencies and possibly reducing them. The current study tries to understand the use of alternate data for prediction of loan delinquen