Credit risk

Using AI for Credit Risk Assessment to Improve Results

With American consumer debt climb to over $ 14 trillion, to avoid costly defaults, lenders need to filter credit risk more accurately than ever.

Traditional portfolio analysis is not enough to understand the complex factors that contribute to credit risk. Instead, lenders need to take a more holistic approach to assessing the financial health of borrowers, starting earlier in the client’s life cycle. And with a seamless customer experience at a premium across the financial industry, lenders need to ensure that the solution they choose fits seamlessly with their existing offerings.

Adopting artificial intelligence (AI) for credit risk assessment can convert huge volumes of real-time customer behavioral and financial data into useful information. By predicting outcomes such as customers who may be offered higher credit limits, AI can increase revenue and profitability while maintaining a top-notch customer experience.

When it comes to assessing credit risk, many FIs use outdated techniques. Forty percent still use legacy rule-based systems, and 26 percent use manual exams. While business rule management systems offer some automation, they are too rigid to adapt and learn over time, nor can they scale with the exponential growth of financial data.

Although AI presents a more flexible solution, many FIs are still hesitant to use it to assess credit risk. It is true that it is difficult to recruit talent in AI, and that building and training internal models takes time. On the other end of the spectrum, out-of-the-box AI solutions are easy to implement, but often not fit enough. Another option is to work with a partner to develop custom AI models.

The benefits of AI for credit risk assessment will be huge. With the right models in place, your organization can expedite credit applications and predict defaults months in advance. And by leveraging the available data, you can do all of this without adding more fill-out forms or other sources of customer experience friction.

Improve the credit decision

The traditional credit decision is based on a limited number of data points, including the rating of credit bureaus and information from a borrower’s application. An AI system can create a more holistic borrower profile by incorporating alternative information such as utility bills and rent payments, as well as data permitted by regulation, such as the credit history of the bank. borrower from other lenders.

This deeper insight into a borrower’s financial health can promote faster decision-making, whether the borrower is a new applicant or an existing customer seeking more credit. It also supports more accurate decision making, especially for thin file clients with little or no credit history.

Anticipate and prevent bad debts

AI systems allow you to score customers more than once a month, allowing for the incorporation of transactional data in real time. The models can incorporate a wide variety of data points, including a customer making payments, when requesting cash advances, and how they use their credit cards.

By identifying patterns of customer behavior, AI models can predict delinquency long before a customer actually misses a payment – or signal a customer who is ready for an increased credit limit.

This information can also help you understand why customers are missing payments and take action accordingly. For example, if a trusted customer misses a payment without any warning signs, they may just need a payment reminder. In contrast, a client who stopped directly depositing their paychecks when they defaulted may have suffered job loss and need more support to get back on track.

Optimize collections

No lender wants to send a debt to collections unless they absolutely have to. Third-party collection agency fees quickly eat away at margins. Plus, most customers won’t come back once they start receiving collection calls – and acquiring a new customer can cost up to 25 times more than to keep an existing one.

With AI, you can use data points collected throughout the customer lifecycle to identify which customers are most likely to repay the balances they owe. From there, you can work to get them back on track, such as offering payment plans or temporarily lowering limits. By intervening proactively, you may be able to save the account before it’s debited, which can build customer loyalty and boost your organization’s bottom line.

The future of finance is transparent, efficient, personalized and powered by AI. By using AI models for credit risk assessment, you’ll get the information you need to make faster, smarter decisions throughout the customer lifecycle.

Sudhir Jha is the head of Brighterion.


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