Redefine redlines, use data analytics to achieve greater financial integration

(Representational image)

Ashish Desai

The journey from financial inclusion to financial integration is not only about making products available and accessible, but also about making them relevant and applicable. Kamal Bai is an entrepreneur with a small restaurant using UPI and a bank account opened under the Pradhan Mantri Jan Dhan Yojana (PMJDY). She has a banking history with a number of transactions, and regular deposits — unfortunately, she does not have access to meaningful and relevant credit products.

For rural women entrepreneurs, capital is primarily accessed through Joint Liability Group (JLG) lending. However, this financial product does not necessary allow women to grow their businesses beyond a certain point for which individual-based short-term credit and cash flow-based lending are essential. Credit products for micro entrepreneurs have typically been structured from a supply side point of view, and not from what is needed from the demand side.

One of the reasons cited for demand side design failures is that a person’s neighbourhood is not ‘supposed’ to be creditworthy. Financial institutions with a lens of traditional credit policy practice ‘redlining’ to avoid bad credit. Redlining can be defined as the systematic denial of service (either direct or indirect) to people belonging to groups or communities by selectively raising prices of financial products. However, financial institutions follow reverse redlining, which is when a lender or insurer targets neighbourhood with inflated interest rates by taking advantage of the lack of lending competition relative to non-redlined neighbourhoods.

Borrowers from lower income segments living in a particular locality are more willing to pay compared with those from affluent ones. While this pattern is widely observed, there persists in emerging markets a notion that people of certain backgrounds or those owning a particular asset class are creditworthy or ‘prime’ customers particularly with reference to unsecured loan.

Under the veil of higher risk and operational cost, financial products for micro entrepreneurs, especially with limited credit history, are deemed mostly unaffordable, except for JLG lending, which has a limited focus on risk-based pricing and product structuring at the individual borrower level.

We have reached a moment when the current models of risk assessment are inadequate for financial integration. This must now be driven by better analytics based on data that gives deeper insights about risk, especially those living in poor neighbourhoods.

Our digital activity leaves a footprint, and collectively, these tiny traces work to create our ‘digital profile’. This includes static information about us, like date of birth, gender, nationality, and dynamic information such as address, place of work, telecom operator data, transaction history, social media engagement, general Internet activity, and consumption behaviour. By unravelling these patterns, we can move away from traditional segmentation, and risk profiling.

Conventionally, segments were defined based on static parameters, such as demographics, geography, income level, etc. Demographic segmentation in combination with transactional analytics can, in turn, offer unparalleled insights. Having a similar demographic profile does not necessarily mean similar behaviour. Take three examples:

Parvati is 45-years-old, has two children, and lives in Delhi with her husband. She has passed Class 10, owns a small vegetable vending stall, earns Rs 20,000 a month, and remits Rs 4,000 each month to her parents in Karnataka. She visits her bank branch to transfer money, does a recurring deposit of Rs 1,000 a month, and offers UPI payments to her customers.

Jyoti is 33-years-old, and runs a small corner shop in Mumbai. She has cleared Class 12, earns Rs 25,000 a month, and digitally remits Rs 6,000 to her mother in Bihar every month. She does not have a recurring deposit, and does not offer UPI payments to her customers. Radha is 35-years-old undergraduate who manages a food stall in Delhi. She earns Rs 25,000 a month, and sends Rs 5,000 every month to his family in Bihar. She visits her bank branches for remittance, offers UPI to her customers, and does a recurring deposit of Rs 5000 per month.

Even though Jyoti and Radha are more or less the same age, are from the same state, and earn the same, their behaviour is different. While Parvati and Radha are from different demographic segments, they are in fact more similar in behaviour. Analytics combined with strategic bundling of products allows low cost, real-time delivery of personalised, valuable products that help micro entrepreneurs realise their aspirations, and can allow lenders to track early warning signals of fraud, or to track repayment ability or willingness.

With JAM (Jan Dhan-Aadhaar-Mobile) making considerable progress, the micro finance industry should now apply advances in analytics to achieve financial integration. However, while doing so, privacy laws, consumer rights as well as ethics in data analytics should be maintained. The right balance will result in sustainable growth opportunities for the financial services industry, and have products which are accessible, applicable, affordable, and acceptable for micro entrepreneurs.

Ashish Desai is Director - Monitoring and Evaluation, Development Monitoring and Evaluation Office, NITI Aayog. Views are personal, and do not represent the stand of this publication.