As a veteran of the banking industry, I know that it is not uncommon to find a 25-year-old applying for a loan for a swanky motorbike or a high-end mobile phone. Millennials who have a few years of work under their belt and no credit history, or whom we call the new-to-credit segment, find that their loans are either not approved, or come at high rates of interest. I am often asked for advice about how people can ensure that their loans are approved and how they can get the best rates.
Traditional lending institutions in India are conservative. Their assessment of the credit-worthiness of an individual is restricted to narrow criteria, which require you to have a credit footprint and strong credit history.
However, as banks and financial institutions look at expanding their reach, they are also looking at more effective ways of assessing the credit risk of loan applicants. With powerful data analytics and artificial intelligence tools, they have expanded the pool of possible parameters that can be assessed to determine the creditworthiness of a potential borrower in a more holistic manner.
A straightforward example would be whether a customer pays phone bills/utility bills on time or is a frequent defaulter, which could pointing towards stability and intent. A more unorthodox example would be using the location information of the mobile phone of a customer, which can be a strong indicator for verification of residence and office addresses.
The analysis of a person’s social media activity can be an indicator of their lifestyle, as well as their income and spending patterns. The increased influx of social media in our lives has created a significant bank of data not only about ourselves, but also about the people we associate with.
The credit profiles of your network of closest ‘friends’ can be an important factor in determining your own credit score too. Additionally, an individual having an active social media profile, and yet listing a different place of employment as compared to that in their application, can throw up a red flag. Your LinkedIn profile can also viewed as a measure of your professional stability, and can be an indicator of your loan repayment ability.
There has been a significant rise in availability of consumer data due to the exponential growth in internet and mobile penetration. The data we leave behind, whether while registering on online portals, making purchases, downloading apps or signing up for newsletters; is also more varied and ‘dense’ than ever before. This has created a vast platform to track customers’ online presence to access their behaviour, intent and most importantly, creditworthiness. It is also becoming increasingly cost-efficient for companies to build credit risk profiles of potential borrowers, as people’s data is becoming more accessible. Not just retail loans, but disbursal of small business loans too is being driven by analysis of alternative data.
We are seeing trends where access to credit in today’s world is being driven by technology, rather than by banks and other financial institutions. Your digital footprints are changing the rules of the credit game. In this scenario, it becomes imperative to ensure that your digital footprints paint a reliable and trustworthy picture to a prospective lender. Although banks still predominantly rely on traditional data to assess loan applications, the pace at which machine learning and predictive intelligence is growing, it won’t be very long before this alternative data is considered a standard part of arriving at a credit scoring.
Rajiv Raj is co-founder and director of CreditVidya.
