With rising Internet penetration and digitisation, new-age financial services like digital lending, e-Wallets, phygital banking, BNPL, and other fintech innovations are powering financial inclusion in India.
However, access to credit is still shadowed by archaic underwriting systems. This is an important peg of an inclusive financial ecosystem that can bolster India’s growth story, positively affecting economic development and GDP.
Restricted access to credit threatens equitable distribution of income and wealth, and affects macroeconomic stability. Having equal opportunity to get credit when needed, on the other hand, is vital.
It helps the underserved to embrace entrepreneurial opportunities, insure against risks, invest in education, and take all the other steps that contribute to individual growth. With 190 million people in India still unbanked, the sheer numbers pose a large barrier to financial inclusiveness.
The Indian government has taken key steps to improve access to credit and spur growth, especially for MSMEs through schemes like credit guarantee scheme (CGS), and for borrowers looking for housing finance through liquidity infusion facility (Lift). However, these measures contributed more towards solving the liquidity crunch than how access to credit itself is measured. And that’s where traditional credit scoring comes under the spotlight.
Here’s why alternative credit scoring measures are the need of the hour. They can actually catalyse a positive transformation in India’s financial ecosystem, driving both inclusion and equitability like never before.
Bottlenecks In Traditional Credit Scoring Models
Scrutinising credit exposure and utilisation, as well as repayment history, credit scoring is not quite the anti-hero it is often made out to be. After all, such markers reveal ability and willingness to repay, thereby reducing default, making credit affordable for high scorers, and providing an unbiased system that expands access to credit. But does it really achieve these goals?
Roadblocks built into the current system inherently exclude thin-file borrowers who are either new to credit, new earners, have limited or no credit history, are underbanked, or simply unbanked. To empower these previously ‘credit invisible’ customers and bring them into the formal financial fold, the way financial institutions assess creditworthiness needs to undergo a sea change. This is where data can unlock terabytes of economic opportunity.
How Data Can Be A Game-Changer
Thanks to artificial intelligence (AI) and machine learning (ML), the two big boons of digitalisation, fintechs are now focussing on formulating predictive models for alternative credit scoring, based on data collected from different behavioural attributes of potential customers.
The two major benefits of digitalisation are artificial intelligence (AI) and machine learning (ML).
By relying on easily traceable factors, such as monthly utility bills, emails, social media usage, contact lists, and GPS data in smartphones, as well as psychological factors determined by psychometric testing, this use of data has the potential to overcome the challenges of traditional credit scoring mechanisms.
This not only empowers customers who don’t have prior experience with credit, but also opens up new avenues of lending for financial institutions, resulting in a win-win situation. Moreover, these scoring models not only reduce lending risk, but also boost revenue. In fact, the ability of data to create strong alternate credit scoring models based on digital footprints has been explored globally.
In 2015, a report by Omidyar Network revealed that big data is poised to help 325–580 million people access to credit for the first time among the world’s six best-emerging markets, including India, Mexico, Brazil, and Indonesia. In 2017, Thailand’s oldest and largest bank used non-traditional data to create new credit scoring mechanisms to lend to thin-file borrowers. In Chile, Destacame, a digital and alternative credit scoring platform works with more than 35 financial institutions, serving more than 2.6 million customers! Thus, harnessing AI and predictive analytics to leverage alternative data has already proven to work wonders in driving financial inclusion.
Implications Of Alternative Credit Scoring From ‘Glocal’ Lens
Though Indian fintechs have already begun using alternative credit scoring systems, privacy is an important concern. After all, creating digital scorecards to screen for credit repayment involves getting permission to track data that customers may not want to share. Using historic data that may come with its own bias in training ML/AI models can also pose a challenge, as companies will need to ensure this doesn’t exclude unserved or underserved customers. In India, women especially may have lower scores if data related to the number of contacts on mobiles is analysed for instance, as men enjoy higher social mobility.
Despite these concerns, alternative credit scoring that uses smartphone data is the way forward toward greater financial inclusion. After all, there were 227 million active Internet users in rural India and 205 million in urban India as of 2020.
For smooth sailing towards a more inclusive future, fintechs can and should use every opportunity to ensure that their alternate credit scoring mechanisms are transparent about how they determine creditworthiness, remain free from discrimination, and ensure privacy and security of data. By building trust on both sides of the equation and enabling smart lending, alternate credit scoring can bring about a ground-breaking spike in the country’s financial inclusion index.
The author is vice chairman, MD CASHe
(Disclaimer: Views expressed are the author’s own, and Outlook Money does not necessarily subscribe to them. Outlook Money shall not be responsible for any damage caused to any person/organisation directly or indirectly.)
