Analytics and machine learning can pave the way for safe digital lending


By Monish Anand, Founder & CEO, MyShubhLife

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

As digital lending evolves into lower-risk lending, conventional dashboards are invariably unable to handle entire lending cycles. Data analysis helps lenders better understand client/borrower needs and capabilities. Additionally, analytics and machine learning provide the means to identify patterns in data that can help differentiate between good (low-risk) and bad (high-risk) borrowers. In other words, assessing borrower creditworthiness is made possible by examining large amounts of data and leveraging the power of advanced analytics along with machine learning algorithms to learn complex patterns until here unknowns, which help to distinguish a good borrower from a bad one, in the form of a credit risk model (CRM).

Modern lenders using advanced fintech platforms harness the power of these algorithms by accessing data from various sources such as
– Client Desk – enquiries, trade lines, sanctioned loans, late payments, etc.
– Bank statements – monthly income, bank balance, other monthly lender debits, etc.
– Demographics and miscellaneous – degree, life status, profession, etc.

Data analytics coupled with machine learning helps create creditworthiness bands – from low-risk regions to high-risk regions – where each ranked band is representative of the borrower’s ability to repay the loan once disbursed . This process of creating groups of borrowers not only serves to guarantee safe digital loans (since the lender can decide to exclude certain high-risk groups from their portfolio), but also incorporates sufficient intelligence to include medium-risk borrowers. that might otherwise be rejected by conventional loan dashboards. . With businesses needing to be fast yet accurate, data analytics along with powerful learning algorithms are paramount to developing and deploying such high-fidelity credit risk models.

The science of data-driven digital lending lends itself perfectly to the skill of robust Early Warning Systems (EWS) that predict a borrower’s ability to make monthly repayments on the disbursed loan. Such predictive models are able, with an acceptable degree of accuracy, to identify borrowers based on their intention to repay or lack thereof relative to their repayment capacity, over a monthly interval. With this refined insight proactively generated by predictive models, collections staff can now allocate and prioritize their resources to focus their efforts on borrowers who show a high likelihood of questionable intent to repay.

The powerful combination of advanced credit risk models and robust early warning systems has enabled digital lenders to expand their segment of borrowers to lend to while being able to significantly hedge their lending risk. As data brings more precision to digital lending, Preferred Customers also benefit from lower interest rates, better financial literacy, and more attention to borrowing. Lenders can now dive into specific scenarios to verify borrower eligibility.

In order to retain customers and attract new customers to digital printing, it is important to focus more on digital engagement and personalization rather than transformation. This is also made possible through data analysis. Keeping in mind the needs of our clients, we can now provide personalized financial services and optimize our market by being up to date with our target segment. This helps create a safe ecosystem for lending. Therefore, personalization and automation play a key role in reassuring customers that they are being served by personalized services according to their personal preferences.

Lenders need to be as responsive and nimble as possible by creating new business models, using data trends and targeting new customer segments to future-proof their strategies to ensure business continuity. This requires constant monitoring and validation of the models and, where necessary, refurbishment of these models with new data or simply refinement and readjustment of the models. To ensure that businesses remain innovative and compliant with the regulatory framework, personalization and automation will be essential to spot upcoming market trends, recognize potential risks and identify future actions. This in turn helps impart financial knowledge to customers so that they are more attentive to borrowing.

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