According to the latest World Bank data, every one in three adults in the world are still unbanked. And almost 50% of these 1.7 billion people left outside the realms of the traditional banking system happen to be women.2 Interestingly, women also make up 80% of the first-time borrowers of microfinance credit products.3 A part of the population who are unable to obtain credit otherwise due to lack of financial independence, social norms, and more such socio-economic challenges.
The intent of the microfinance system is to provide small, sachet-sized group and individual loans (<$100 in the majority of the countries and <=$50,000 in the USA) in an effort to enable inclusive economic growth.
Though these loans follow a standardized repayment structure, the pandemic-induced financial disruption derailed all the repayment options leaving a devastating impact on the collection efficiency of lenders. As a direct result, the asset quality of MFIs across the globe witnessed a sharp deterioration.
While the 2020 disruption has been an eye-opener for the entire financial world in so many ways, for the microfinance sector, it showed the pressing need for a tech-driven collection system. Loan collection is so much more than sending borrowers timely reminders or assessing risks. Some factors always remain beyond our control. Hence, MFIs need to leverage the latest technologies in order to create an effective loan collection system that can help navigate possible defaults. Adoption of machine learning has shown remarkable impact in analyzing borrowers’ insights and identifying relevant patterns.
Machine learning, when combined with artificial intelligence, can also examine a borrower’s digital interactions to identify variables and indicate an impending financial crisis in advance. Variables such as a decrease in online transactions or no salary received in the last few months can play a significant role in assessing possible risk in an effective way. In fact, this can be more effective than a static credit score.
ML models are capable of rapidly incorporating latest data as circumstances change in the borrower’s life and determining delinquency even before that happens. This insight can help MFIs take timely measures to prevent delinquency and protect their cash flow liquidity and segment borrowers on the basis of risk factors. Accordingly, informed financial decisions can be taken such bringing down the EMIs to uphold the interests of both borrowers and lenders.
Furthermore, ML-powered tools can also recommend real-time resource allocation suggestions or alternate dates for due payments.
Machine learning combined with deep learning can evaluate an individual borrower’s credit risk performance more effectively than traditional methods that tend to use linear regression models. Machine and deep learning models offer far more accurate prediction outcomes and can model credit risk even in the absence of a credit history or a centralized database. And every time the ML-powered solution improves the quality of a decision, it automatically updates itself to serve more customers in a better way.
Intelligence of machine coupled with the understanding of a human being
As stated above, the goal of microfinance is not to leave behind but to enable and empower. The precision analysis of machine learning must be combined with the humane understanding in order to ensure these solutions serve the core purpose and not inadvertently make credit products out of reach for a majority of the population. While banks across the globe have extended their support in these uncertain times through new moratorium clauses, for borrowers who have been struggling with unemployment woes, moratorium measures fail to offer them the desired comfort.
To navigate these stressful scenarios, MFIs must invest in technologies such as artificial intelligence, machine learning, automation, and data analytics to create a collection system that’s highly efficient yet inclusive and can support MFIs in real-time decision making enabling the growth for themselves as well as billions of potential customers across the world. A primary step to achieving the same could be modernizing all static and dynamic dataflows on the cloud. This provides an in-depth overview. Collect, store, analyze, and leverage data for maximum business benefits.
