Using Machine Learning to Predict and Reduce Delinquency for Card/Loan Portfolios

Providing loans or credit is a fundamental business model of any financial institution with a banking license. The interest earned forms the major revenue component of their operations. However, things are not that easy when it comes to qualifying applicants for providing loans. Traditional underwriting approaches have truly lived their age and are almost incapable of providing an accurate risk score for an applicant.

In a large bank, this flawed approach can quickly escalate into a scenario where too many loans are sanctioned to people with poor repayment capacity. Such a scenario was one of the driving forces behind the 2008 global recession and eliminating the chances of history repeating is of uttermost importance for lenders. In other words, they must ensure that loan portfolios do not turn delinquent at all costs which is a tough challenge. In the US alone, 2.68 percent of all consumer loans in force for the first quarter of 2024 have turned delinquent according to data.

The Key Villain – Card Loan Portfolios

One of the biggest contributors to this loan repayment problem is the rise of credit card debt. Most consumer loans are now availed through credit instruments like credit cards which are issued by banks or even their fintech partners. The loans issued either as direct finance or through purchase-based finance schemes are scrutinized way less when compared to an actual personal loan or credit mechanism. The result is the explosive scale of financing we see today with a greater threat of delinquency hanging above the heads of credit issuers.

The Solution to Card Loan Delinquency: Machine Learning

In an era where AI is becoming an integral part of all customer and operational experiences for businesses, it is not strange for banks to turn towards the same to help bring down their card loan delinquency rates. Machine learning can greatly influence how lenders qualify applicants. It can strike a balance between risk scores and actual creditworthiness. This will help in ensuring that potential opportunities are not missed owing to hard emphasis on a negligible delinquency history of a borrower.

How Can Machine Learning Help in Reducing Card Loan Portfolios?

With machine learning, financiers can get a holistic and granular view of the credit profile of an applicant. It works by crunching together and analyzing data from credit bureaus, transactional records of the applicant, their occupation, history of residence with duration of stay, and much more. This helps in preparing a more realistic credit or risk score for the applicant which can be significantly different from the ones that are simply calculated on past delinquency rates from bureau records.

Let us explore some of the important ways in which machine learning helps in predicting and reducing delinquency for card loan portfolios:

·       Accurate Underwriting

The best way to prevent erratic lending practices via cards is to ensure a stricter underwriting process that helps in weeding out risks but not in a blanket manner that compromises profitability. As explained earlier, machine learning solutions can help in detecting a more granular risk profile of a loan applicant.

Their card utilization rates, repayment schedules, purchase or shopping behavior, etc. can be analyzed to arrive at a better probability of delinquency and then ultimately help them avail of the loan or be rejected due to high risks.

·       Continuous Learning

A credit card holder may not qualify for a loan scheme today. But that doesn’t mean he or she is to be forever denied a loan offering. A person’s financial history is an ever-evolving cycle. What lenders need to do is to be able to learn how their financial prowess is improving over time and take steps to make loans available in the future. This is possible when machine learning-based lending solutions or models are used in their core operational systems.

The applicant’s financial discipline can be analyzed to help improve their chances of securing credit that was once denied for risk elements. This continuous learning model for credit provision is a guarantee that machine learning can provide for lenders.

·       Bias-free Credit Provisioning

When ML-powered lending models have enough data, they can transition into autonomous credit systems that can evaluate applicants, assess their risk profile, predict delinquency rates, and approve loan disbursal without any human involvement. Since the entire process is data-driven, the decision to provide the loan will have a lower risk than traditional disbursal techniques that are known to be influenced by bias.

Such a system will ensure that all applicants are treated fairly and that corruption at the staff level does not impact the lender’s financial prudence. In other words, machine learning-based solutions can also act as a powerful guard against corruption.

A Win-Win for Everyone

With machine learning, lenders can gauge risk profiles more accurately and ensure that no worthy candidate is denied a credit line on their cards because of a simple or negligible credit risk. The ML-powered lending solution’s autonomous workflow ensures that customers do not have to wait for days to availing credit lines. Processes can be instantaneous, leading to greatly improved customer experience.

Ultimately using machine learning to predict and reduce delinquency for card loan portfolios is a win-win situation for all parties involved. What lenders need to strategize is the selection of the right tools or technologies to build their intelligent lending infrastructure. This is where an expert partner like Verinite can make a huge difference. Get in touch with us to know more.