How big data can optimise the microfinance sector

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In India, rapid advances in technology, mobile phone penetration, new players and massive investments in financial technology are transforming the financial services landscape. The big question is how big data can enable microfinance institutions (MFIs) play an innovative role in the new ecosystem, while meeting evolving customer expectations and needs, by incorporating digitisation and partnering with the other stakeholders in the ecosystem.

The microfinance sector is estimated to be serving close to 200 million end clients with a credit portfolio outstanding of over Rs 2.5 lakh crore across the Joint Liability Group (JLG) and Self Help Group (SHG) mode of lending as of the end of March, 2019. With last-mile connectivity established to reach over 200 million clients at least once a month, it is fast evolving into a powerful channel to achieve one of the national priorities –financial inclusion.

One good thing about microfinance is the availability of huge amount of granular data, considering 200 million customer accounts spread across the country. This data set is quite rich, covering age, gender, occupation, income estimates, repayment patterns, attendance at meeting centres and others. With most credit institutions operating on IT-enabled lending platforms and four active credit bureaus being updated with all the data, four key aspects are evolving:

Risk and data based credit decisions This can be used to effectively develop application scorecards for effective and unbiased selection of customers for funding. Key information and customer behaviour patterns derived from industry data can also be baked into such scorecards. At the front end, such credit decisions are available through digital channels of the handheld device of the customer service officer, in a matter of seconds. They can thus execute the role of a fulfillment executive, who spends effective time in verification, validation and reference checks. Automatic big data-churned algorithm-based customer loan request processing, approval and disbursement by crediting of the customer’s account in seconds, is probably what the crystal ball is projecting.

Product development and selection
Big data churning at the back-end, using cutting edge statistical models and analytics, artificial intelligence and machine learning can be effectively harnessed to service customers just-in-time for their financial needs, with just the right loan ticket size and repayment frequencies. It also allows one to look into the future in predicting the portfolio behaviours at various geographies and segments and hence, predict likely defaults or credit losses more accurately. This will enable correct risk pricing of the loans being offered; thereby charging optimised interest rates to customers, in line with the international regulatory standards being mandated by central banks and regulators around the world.

Product or service positioning
In the near future, through big data, the industry will be in a position to offer products that the customer needs rather than products that are designed internally. This would help in cross-selling of financial inclusion products, services like bank accounts, pensions, insurance, investments and remittances etc, by indicating the needs of the customers generated through analytics – data modelling, customer segmentation and price modelling.

M-commerce and e-payments
With the help of big data, m-commerce will allow the customers to choose services on credit by enabling a loan approval mechanism through analytics. Big data will also help bring payment options together. An estimate puts over a billion transactions happening every year in the sector, which can be e-enabled to benefit customers and bring operating expenses down.

We have all heard about “work smarter not work harder”. Big data enables just that. Various data, structured and unstructured, available in the universe is fed into the network to analyse and response effectively and quickly to problem statements. Big data coupled with various facets of artificial intelligence (AI) have already started to change the way decisions are taken. However, it is equally important to understand the relevance of analysing the right data and eliminating the garbage. Imagine what it could do to a sector where the current level of inclusion across FI products and services is just about 20 percent in a country with a population of 1.3 billion and growing. It could lead to the ‘Uberisation’ of credit.

The author is the MD of Arohan Financial Services Limited, an Aavishkaar Group company

The thoughts and opinions shared here are of the author.