Gen AI has use cases throughout lending and credit processes

Banks and credit unions already have a trove of historical financial data on their customers, from credit cards to loans, and everyday deposit account activity.

Leveraging this data effectively, integrating it with diverse data sources, and performing comprehensive risk analyses are crucial steps toward maintaining a competitive edge, including in loan and credit markets. To achieve this, banks and other financial institutions can tap generative artificial intelligence (gen AI).

The AI revolution within the banking industry is already taking place but knowing where and how AI can be best utilized is integral to its success. According to a survey by The Economist, over 85% of banks have a clear strategy in place to implement AI into their services, but there are still more organizations that need help navigating the latest in this technology.

Evidence points to Gen AI enabling institutions of all sizes to enhance efficiency, reduce risks, improve customer service and boost the ability of staff to perform at high levels, including along multi-step lending and credit approval processes. AI will only continue to prove its worth in positioning banks and credit unions for success in a rapidly evolving financial landscape.

For financial institutions interested in building out a gen AI strategy, there are a few areas to target.

Identity verification

Biometric authentication, such as facial recognition, is used to verify the identity of individual customers. AI agents can scrape the web to validate the association of individuals with the bank’s business customers. AI also authenticates documents, such as PDF bank statements, used for credit analysis, ensuring the legitimacy of financial data. This helps verify an applicant’s self-reported data, such as revenue size, profitability, and industry, reducing the risk of fraud and money laundering. The technology will assist banks in eliminating manual processes from account opening and allow banks to onboard customers digitally.

Transaction monitoring, alerts and reporting

Real-time credit transaction monitoring identifies unusual behaviors and patterns that deviate from the norm. Immediate alerts for suspicious transactions are sent to analysts, allowing for rapid investigation and response. AI compiles reports like Unusual Activity Reports (UARs) and Suspicious Activity Reports (SARs), automating the process to ensure accuracy and timeliness. Real-time data processing of bank transactions allows for ongoing cash flow analysis. By analyzing cash flow behavior patterns, AI predicts future behavior, enabling dynamic adjustments to credit limits. If adverse actions are needed, AI can help send these notices to customers accurately and timely, ensuring consistency and regulatory compliance.

Underwriting

Predictive modeling assesses the probability of default for bank customers based on a wide array of variables. The introduction of gen AI allows banks to aggregate data from multiple sources, both traditional and alternative, allowing for tailored credit limits based on the customer’s financial situation and needs. As more data is collected and new data introduced, AI models continuously learn and improve accuracy and predictive power over time. Natural language processing (NLP) is used to write credit approval memos, drastically cutting down underwriting time for management overviews, industry analysis, risks and mitigants.

Customer identification and credit underwriting automation

Gen AI automates the customer identification process and initial credit underwriting process, reducing the time and effort required for manual reviews. Automated decision-making ensures consistency and objectivity in credit assessments, reducing human bias. This significantly shortens the time required for credit approval, leading to quicker customer ramp-up periods. By providing transparent and data-driven decisions, AI helps banks demonstrate compliance with regulations.

Brendan Coons is Director of Risk at Torpago.