In India, a young person who has just started a new job, or a middle-aged, blue-collar worker who has never taken a bank loan, often face rejections when they apply for credit due to the lack of a credit repayment history.
However, such people might be paying their other financial obligations on time and be fairly capable of repaying their equalised monthly installments (EMIs). For instance, the young individual might be paying his postpaid mobile bills on time and the blue-collar worker might be paying the electricity and other utility bills on time.
Such data points can provide important information about how people handle their financial obligations and how disciplined they are in making payments on time. This is the avenue of alternate data.
Financial institutions across India and globally are exploring alternative data to obtain insights about money handling and draw inferences for credit risk assessment. The quantum of digital transactions that we all engage in today and the data processing abilities through alternative approaches are creating newer avenues for both lenders and the underserved borrowers. Insights brought by alternative data approaches about borrower behaviour can fortify the traditional approaches of credit risk assessment.
Sources of alternative data
A credit repayment history is collected by credit bureaus across all credits. Currently, financial institutions request the credit bureau to provide a credit score for deciding whether the applicant should be granted a loan and will be able to repay it. This is called credit risk assessment, which can often become a vicious cycle between the struggle to get a loan and repay it to create a credit history.
But with internet and mobile banking upping the game, it is possible to document financial transactions such as utility payment cycles, monthly statements, e-commerce transactions, mobile recharge/bill payments, and so on. Analysing these can help assess the creditworthiness of a borrower.
Lenders can use alternative data in order to understand the financial behavioural pattern and intent to repay of a borrower by understanding their payments for telecom bills, utility bills, mobile wallets e-commerce etc.
When it comes to intent to repay, financial institutions can use psychometrics. Psychometrics is a personality assessment tool that supports the financial institution to take a decision for approval or decline basis personality assessment of an individual. This can also provide valuable inputs about the future of micro small medium enterprises (MSMEs) by assessing the personality of the owner/authorised signatories of MSME business.
Power of psychometrics
Many players are willing to explore the potential of psychometrics to extract interesting personal insights about lenders. This becomes critical for first-time borrowers, thin-file borrowers, and areas with emerging or no credit bureaus. Psychometrics can help map credit risk based on an individual’s personality.
This, along with the traditional lender evaluation processes, can enhance decision-making. It involves the use of a quiz that has simple questions based on common daily situations. The way a person responds can give a view of the borrowers’ creditworthiness and loan repayment intent.
AI, machine learning and big data
The creditworthiness of a borrower can be extracted from data beyond loan repayment and income. However, the sheer volume of data available poses a challenge in getting useful inferences.
Big data technology handles these large amounts of data. It has the potential to uncover the trends of the customer’s behaviour and buying patterns. The customer’s various transactions such as grocery buying, utility bill payments, transferring money to family and friends, or even buying tickets for a movie, can all be combined to provide an all-round view into their financial behaviour and repayment capacity.
Financial institutions are now applying advanced analytics, artificial intelligence (AI), and machine learning techniques to expand the creditworthy envelope beyond traditional credit payment history.
Alternative data has the potential to iron out some key challenges faced by the credit sector. Inclusion of algorithms, data science, and AI will fuel the growth of alternative data. Having enough understanding of the concept will promote financial inclusion and encourage institutions and individuals to come forward and improve their credit scores. With appropriate regularisation, alternative data combined with the expertise of analytics can empower the credit ecosystem checking fraudulent activities and driving smart lending.
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