For the financial sector, 2023 has been a tough year. Historic inflation and aggressive monetary policy have limited purchasing power. Meanwhile, the closures of Silicon Valley Bank, First Republic Bank, and Signature Bank have created mistrust and fervor among businesses and consumers alike. Trepidation abounds, and it’s adding to credit concerns over the growing need to borrow and uncertainties about how to pay it back.
As companies look closer at how to avoid credit risks, they’re increasingly turning to artificial intelligence (AI) to help them make calculated decisions for the betterment of their balance sheets.
The evolving approach to credit risk management
Credit risk is a constant concern for any lender, and it quickly becomes the central focus during times of economic strife. It’s easier to institute new underwriting safeguards and raise qualifications for borrowers in tandem with economic shifts. But there’s still considerable risk in the form of credit already issued. Is there a higher potential for default among borrowers whose situations have changed relative to the terms of their loan? If so, they’re now a credit risk.
Traditional credit risk management strategies are largely reactive. For example, transferring risk to a different lender often means unloading a loan short of its term (and its profitability). Moreover, many lenders aren’t able to identify borrowers at risk for default until the signs are already present.
Now, as we encounter economic turbulence, many businesses are keyed in on credit risk. But instead of waiting for defaults to roll in, they’re turning to AI to help them be proactive in spotting and addressing at-risk accounts.
The role of AI in credit risk management
In contrast to the financial sector, AI has had a banner year in 2023. Generative AI and robotic process automation (RPA) have been transformative, spurring transcendent innovations across industries. Businesses that have embraced this technology have seen firsthand AI’s ability to harness the wealth of data driving the banking and lending industry.
When managing credit risks, AI has tremendous potential. Early use cases for AI in credit risk management include:
- Credit scoring and decision making: AI-powered models can analyze vast amounts of data to find patterns and make predictions about credit risks. It can also recommend helpful interventions to reduce the risk of default for high-risk accounts.
- Fraud detection and prevention: AI can distinguish patterns of suspicious activity, such as multiple applications from the same IP address or sudden changes in spending habits. Tamping down on suspicious behavior is crucial for risk management.
- Portfolio analysis and diversification: AI can analyze a company’s portfolio and identify areas of risk. This information can then be used to diversify the portfolio and reduce the risk of losses.
- Early warning systems: AI can evaluate a borrower’s financial data and recognize patterns indicating default risks. If the model discovers any red flags, it can alert the lender to act proactively before failure to repay.
AI offers distinct benefits within each of these risk mitigation avenues. From leveraging data into informed decision-making to building and training models specific to credit products or company profiles, businesses can more readily preempt credit risk before it’s realized.
AI’s challenges and limitations
While AI is giving companies an edge in managing and mitigating credit risk, expert intervention is still required to leverage this technology to its fullest potential. AI models are only as good as the data they’re given, and few AI models are effective as plug-and-play solutions. Businesses must sanitize the data they’re using to manage risk and build processes to deliver qualified results.
With careful consideration against AI bias, opaque data sources, and generic decisioning, companies can focus on harnessing AI to address specific credit risk challenges. Combined with time-tested risk mitigation methods, AI can help businesses act quickly before they’re faced with defaults and the rippling implications that follow.
Address default implications before they happen
Loan default — even the possibility for default — carries major implications. Mitigating these risks starts by establishing safeguards to promote responsible lending, even amid an increasingly complex and cumbersome financial landscape. AI has the power to intervene early in the evaluation process and promote better risk management right from the outset.