How advanced analytics help lenders prevent delinquency

The need for finance has grown significantly as the interdependence and interconnectivity of the world economies rise.

The number of retail, SME, and business borrowers has multiplied in the past 10 years, changing the debt market dynamics dramatically. India ranks third among emerging markets in terms of total debt of $4.65 trillion, which is spread across the private sector ($2.55 trillion), non-financial corporate debt ($1.54 trillion), and household debt ($1.01 trillion).

Though this upward spiral in credit distribution has increased revenue and profits for financial institutions, loan defaults have risen, primarily due to the economic downturn following the COVID-19 pandemic.

The turbulent times have driven the lending and debt collection sector to move from traditional risk models that typically use a one-size-fits-all approach to a data-backed approach focusing on customer segmentation and personalisation for collections and delinquency predictions.

Leveraging the power of advanced analytics

Delinquency prediction aids lenders in assessing risk by watching and analysing a sizable group of consumers and their financial actions with the use of statistical models that assist in removing biases and errors to provide a score that is close to perfect.

In a country like India, where absolute debt numbers are high and expected to rise with the surging population, it is imperative to adopt advanced analytics-enabled collection models.

Artificial Intelligence (AI) and Machine Learning (ML) provide profound insights and help establish a consumer-centric strategy. Personalised loan behaviour prediction helps determine whether credit lines and loans will be renewed and whether consumers can adhere to their payback plan.

For instance, demographics, credit cards, loans, and transaction data are essential information that must be provided on any loan application. A few additional elements like credit score, interest rate, annual income, and a debt-to-income ratio of the borrower, amongst others, are considered to analyse and determine the loan's success rate thoroughly.

Lenders gain precise information about their self-cure and high-risk customer segment, which can be further divided into micro segments.

The new-age models build a value-at-risk strategy rather than applying predefined criteria and help achieve the goal of treating each consumer with personalised services.

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AI and ML-backed debt recovery ecosystem

The decision-making process does not benefit from the vast amounts of client data that banks have historically gathered, as it is frequently unstructured. A robust, tech-driven credit rating system that aids lenders in reducing credit risks and weeding out problematic instances early on are urgently needed.

Here, fintechs make a significant contribution by working with banks and other financial institutions to develop digital ecosystems for risk measurement that are transparent and auditable, backed by AI and ML algorithms.

Large data sets are transformed into insightful information by fintech organisations using ML techniques. This reservoir also contains credit bureau and alternate data such as the prospective borrower's educational background, employment history, daily transactions, utility, and recurring payments. The structured data creates a loan distribution model that boosts confidence, lowers risks, and acquisition costs.

These cutting-edge fintechs also use AI and ML to speed the lending process while still adhering to legal requirements. On one hand, ML lowers the cost of debt collection and boosts the rate of collection, and on the other, it provides quick and simple access to loans for today's tech-savvy customers with little to no paperwork.

To prevent potential borrowers from borrowing more than they can afford to repay, digital lenders are utilising this data to offer customised loan products and payment options. The extensive usage of advanced analytics in the loan industry creates a win-win situation for borrowers, lenders, and collectors.

Traditional credit models based on limited data and old formulas fail to capture the dynamics of the post-COVID era.

Thanks to AI and ML, credit underwriters can concentrate on complex parameters like additional contingencies and loss-forecasting strategies. The predictive analytics-backed approach examines quantitative and qualitative risk variables, driving lenders and collectors to move from traditional statistical     classifications to dynamic new-age models.

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)