Analytics & Machine Learning can lead the Path to Safe Digital Lending – Express Computer

By Monish Anand, Founder & CEO, MyShubhLife

Since the financial effects of the pandemic have, in certain ways, altered our daily lives, financial institutions have made unprecedented changes in the way they operate in response to economic uncertainty. It reiterated the importance of agility and speed for the modern lending market and set new expectations around transparency and hybrid working which were not as prevalent before. This renewed interest to invest in technology from both Fintechs and traditional banks is to help offer a complete digital experience for the customers.

As digital lending advances in the direction of risk-reduced lending, conventional scorecards invariably fall short of being able to manage entire loan cycles. Data analytics aids lenders in understanding the needs and capabilities of customers/borrowers better. Furthermore, analytics and machine learning provide the means to identify patterns in the data that can help differentiate between good (low risk) and bad (high risk) borrowers. Differently put, assessing borrower creditworthiness is made possible by combing through large amounts of data and leveraging the power of advanced analytics along with machine learning algorithms to learn, hitherto unknown, complex patterns, that help distinguish a good borrower from a bad one, in the form of a Credit Risk Model (CRM).

Modern lenders using advanced fintech platforms leverage the power of such algorithms by accessing data from various sources such as
– Customer Bureau – enquiries, trade lines, sanctioned loans, loan delinquencies, etc.
– Bank Statements – monthly income, bank balance, other lender monthly debits, etc.
– Demography and Miscellaneous – educational qualification, living status, occupation, etc.

Data analytics coupled with machine learning allows to create creditworthiness bands – low risk to high-risk regions – where each rank-ordered band is representative of the borrower’s ability to service the loan once disbursed. This process of creating borrower bands serves to not only ensure safe digital lending (since the lender can decide to exclude certain high-risk bands from their portfolio) but also incorporates sufficient intelligence to include medium risk borrowers who might otherwise be rejected by conventional lending scorecards. With the need of businesses to be quick yet accurate, data analytics along with powerful learning algorithms is paramount to developing and deploying such high-fidelity credit risk models.

The science of data based digital lending seamlessly lends itself to the purview of robust early warning systems (EWS) that predict a borrowers’ ability to make monthly repayments on the disbursed loan. Such predictive models are able to, with an acceptable degree of accuracy, identify borrowers based on their intention to repay or lack thereof versus their ability to repay, on a monthly interval. With such refined information being proactively generated by predictive models, Collections personnel can now allocate and prioritize their resources so as to focus their efforts on those borrowers who display a high likelihood of a questionable intention to repay.

The powerful combination of advanced credit risk models and robust early warning systems have enabled digital lenders to expand their segment of borrowers to lend to whilst being able to significantly hedge their risk of lending. As data brings more accuracy to digital lending, preferential customers also benefit from lower interest rates, better financial literacy and mindfulness about borrowing. Lenders can now deep dive into specific scenarios to check eligibility of borrowers.

In an attempt to retain customers and attract new customers towards a digital print, it is important to increase their focus on digital engagement and personalisation rather than just on transformation. This is also made possible through data analysis. By keeping in …….


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