India’s 57 million MSMEs employ 120 million people and contribute 45 percent of the nation’s industrial
A less discussed side effect of demonetization is that banks are now sitting on an unprecedented sum of surplus deposits, an amount they are struggling to deploy efficiently. Since demonetization,the central bank has amassed a total of Rs.15.28 lakh crores by the last count in June, meaning that 99 percent ofscrapped currency notes have been deposited. To spur more borrowing, banks have cut interest rates for deposits and loans – even the humble savings account has not been spared, with interest rates being reduced for the first time in six years, from 4 to 3.5 percent.
With the voracious appetite for credit in both India’s MSME sector and the large unbanked/underbanked retail segments, it might seem like a relatively straightforward exercise to match this excess money supply with pent-up demand. But historically, credit has never been readily accessible to the masses. One of the reasons is that processes and regulations, though well-intentioned, have made catering to these segments rather difficult.
Both business and retail consumers suffer as a result of this regulatory bottleneck. India’s 57 million MSMEs employ 120 million people and contribute 45 percent of the nation’s industrial output, but a lack of financing proves to be the biggest impediment to their growth, with the estimated credit gap hovering around Rs 2.93 trillion.
Information asymmetry: A challenge for banks
Though India has established itself as one of the world’s largest emerging markets in the last decade, its informal economy continues to play an essential role in its growth. Since most businesses in the informal economy are cashbased, they have little to none of the paperwork creditors traditionally rely on to assess the risk of the loan. Thus, lenders are unable to resolve the information asymmetry they face and refuse to provide the necessary credit.
Even the few businesses that do keep the required records struggle to generate the cash flow necessary to prove their credibility and offer up collaterals. The lack of traditional documentation makes them unattractive for lenders who are likely to prioritize borrowers who can offer proof of their ability to repay these loans.
The challenges are no different when it comes to procuring a personal loan. The credit risk of an individual borrower is measured based on a CIBIL score range between 300 and 900 – primary indicators being the applicant’s employment stability, monthly salary, and most importantly, documented credit-related activities from formal lenders. EMI and credit card payments, outstanding balance, pending loans on the borrower’s name; these are some of the details used by potential lenders to create a snapshot of a person’s credit-worthiness, all of which an individual in a cash-based economy is unlikely to have.
Alternative data – A new approach to underwriting loans
While it is impossible to guarantee a borrower’s intention to repay a loan, lenders can look beyond financial data and utilize an applicant’s non-traditional data to assess a borrower’s credit risk better. Non-traditional data points from an applicant’s digital footprints include information from social media platforms, e-commerce transactions, bill payments, telecom data, location history, etc… Big data analytics and machine learning can leverage the contextual information and provide insights into the creditworthiness of borrowers. For instance:
• Electricity bills, internet bills or house rental receipts- timely payments suggest the potential borrower has a strong sense of responsibility and is unlikely to be a wilful defaulter
• Social media posts that borrowers write/like/share allow banks to understand borrowers’
interests and thoughts – posts that reflect irresponsible behavior, for example, are potential red flags for lenders
• LinkedIn profiles can help identify a potential borrower’s stability based on the number of jobs held and the time spent at each job
• Consumers’ transaction histories on e-commerce platforms can help banks get a grasp of users’ lifestyle patterns and discretionary incomes
• Psychometric profiles help lenders assess borrowers’ personality traits and indicate their willingness to repay loans
• Digital wallets used by merchants have a trove of data, like the volume and value of their sales, the level of repeat customers, and the number of daily transactions processed – these trends can indicate the long-term viability of a business.
• Social media interactions between a small business and its target audiences demonstrate the quality of relations businesses maintain with their customers.
Ushering in growth
Alternative data has emerged as a promising method of verifying borrowers’ identities as well as their intent and ability to repay. This new age lending process enables traditional lenders to access previously untapped lending opportunities while expanding credit to underserved segments.
Easy access to formal and low-cost credit will contribute significantly to GDP, economic growth and a rise in standard of living. MSMEs currently contribute to 8 percent of GDP, but with access to finance and technology, their contribution is estimated to more than double to 20 percent.The Indian economy is expected to be the world’s second largest by 2040, and financial inclusion will play a
crucial role in attaining this growth.
Fintech start-ups in the past few years have started to intervene on multiple levels to bridge the credit gap and redefine the underwriting process to assess credit risk. Banks and fintech start-ups working together can make huge strides in solving existing bottlenecks, putting assets in the hands of those who can create change in the communities that have invested in them.
Source: EconomicTimes