Traditional portfolio analysis isn’t enough to understand the complex factors contributing to credit risk. Instead, lenders need to take a more holistic approach to assess borrowers’ financial health, starting earlier in the customer lifecycle. And with seamless customer experience at a premium across the financial industry, lenders must ensure the solution they choose integrates smoothly with their existing offerings.
Adopting artificial intelligence (AI) for credit risk assessment can convert enormous volumes of real-time customer behavioral and financial data into useful insights. By predicting outcomes like which customers can be offered higher credit limits, AI can boost revenue and profitability while maintaining a first-rate customer experience.
When it comes to assessing credit risk, many FIs use outdated techniques. Forty percent are still using legacy rules-based systems, and another 26 percent employ manual reviews. While business rules management systems provide some automation, they’re too inflexible to adapt and learn over time, nor can they scale with the exponential growth of financial data.
While AI presents a more flexible solution, many FIs are still hesitant to employ it for assessing credit risk. It’s true that it’s difficult to recruit AI talent, and time-consuming to build and train in-house models. At the other end of the spectrum, off-the-shelf AI solutions are easy to implement, but often aren’t sufficiently tailored. Another option is working with a partner to develop personalized AI models.
The benefits of AI for credit-risk assessment will be enormous. With the right models in place, your organization can speed up credit applications and forecast delinquencies months in advance. And by leveraging available data, you can do all of this without adding more forms to fill out or other sources of friction to the customer experience.
Improve credit decisioning
Traditional credit decisioning relies on a limited number of data points, including scoring from credit bureaus and information from a borrower’s application. An AI system can build a more holistic borrower profile by incorporating alternative information like utility bills and rent payments, as well as regulation-permissible data like the borrower’s credit history with other lenders.
This deeper insight into a borrower’s financial health can support faster decisioning, whether the borrower is a new applicant or an existing customer applying for more credit. It also supports more accurate decisioning, especially for thin file clients with little to no credit history.
Predict and prevent delinquencies
AI systems enable you to score customers more frequently than once a month, enabling the incorporation of real-time transactional data. Models can incorporate a wide range of data points, including a customer makes payments, when they seek cash advances and how they uses their credit cards.
By identifying patterns of customer behavior, AI models can predict delinquency long before a customer actually misses a payment — or flag a customer who’s ready for an increased credit limit.
These insights can also help you understand why customers miss payments and take action accordingly. For example, if a reliable customer misses a payment without any warning signs, they might just need a payment reminder. By contrast, a customer who stopped direct-depositing paychecks around the time of their delinquency might have suffered a job loss and need more support to get back on track.
No lender wants to send a debt to collections if they don’t absolutely have to. Fees from third-party collections agencies quickly eat up margins. Also, most customers won’t return once they start receiving collections calls — and acquiring a new customer can cost up to 25 times more than retaining an existing one.
With AI, you can use data points collected along the entire customer life cycle to identify which customers are most likely to pay back the balances they owe. From there, you can work to get them back on track – for example, by offering payment plans or temporarily decreasing limits. By intervening proactively, you may be able to save the account before it is charged off, which can retain a customer and bolster your organization’s bottom line.
The future of finance is seamless, efficient, personalized — and powered by AI. By employing AI models for credit risk assessment, you’ll gain the insight you need to make faster, smarter decisions across the customer lifecycle.
Jeannette Kescenovitz, who leads development of banking-as-a-service at Finastra, joins us on the BAI Banking Strategies podcast to share her views on how BaaS might grow its presence at U.S. banks and credit unions this year.
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