By Justin Schray; Credit Analyst, Y&A Credit Services

Artificial Intelligence (AI) has been solving problems and answering complex prompts for decades, but today, its presence is ubiquitous. From internet search engines and social media platforms to classrooms and corporate offices, AI has become embedded in our daily lives. Its potential across industries is immense — but how can it specifically benefit financial institutions, particularly in loan underwriting?

Uses and Potential

Traditional underwriting methods are often inconsistent due to the subjective influence of individual analysts’ opinions and judgments. In contrast, AI employs advanced algorithms and machine learning techniques to assess vast datasets and deliver consistent, policy-aligned decisions.

By automating key processes, AI enables banks and other lenders to provide more personalized and responsive service to both prospective and current clients. Through rapid data analysis, AI can process credit scores, tax returns, employment histories, and more, organizing this information in a way that still adheres to sound risk assessment and forecasting standards. As a result, financial institutions spend significantly less time on manual data entry — potentially reducing loan approval timelines from several weeks to just a few days.

Beyond data collection and efficiency, AI also supports institutions in risk assessment and fraud detection. Machine learning models can identify patterns in historical data and monitor them in real time. This enhanced detection capability allows lenders to address potential issues — whether with an individual borrower or an entire industry — much earlier in the lending process.

In addition to decision-making support, AI tools are now capable of generating automated risk reports, financial spreads, and executive summaries. While risk managers remain in control of final lending decisions, AI expedites the process, enabling more timely and data-informed conclusions.

How can AI be implemented?

For a financial institution to effectively implement AI, it must first identify its own operational inefficiencies or pain points.

AI should be adopted with a clear strategic purpose — not simply because it’s a trending technology.

4 questions Institutions must consider:

• What functions will AI support?
• Who will have access to AI tools?
• Do existing policies need to be revised?
• Is current software infrastructure compatible with AI integration?

Establishing a formal roadmap is essential. This includes selecting use cases, identifying key stakeholders, developing integration timelines, and planning staff training. Leadership must champion this transformation to ensure institutional alignment and accountability.

Once the AI system is tested and rolled out, continuous monitoring becomes critical. Risk managers and analysts should review performance data, offer feedback, and contribute to ongoing model improvements. This feedback loop will refine the AI’s accuracy and effectiveness while enhancing the institution’s ability to mitigate credit and operational risk.

Is it safe?

AI systems are designed to manage and interpret large volumes of data, but maintaining the security and privacy of that data is paramount. Financial institutions bear legal and reputational responsibility for safeguarding customer information, and any breach could lead to serious consequences.

One of AI’s strengths is real-time threat detection. AI tools can detect anomalies, flag suspicious behavior, and allow for swift responses. These systems can also predict potential breaches using historical trends and, if configured appropriately, initiate automated responses—such as isolating compromised systems or blocking malicious content.

The U.S. National Security Agency’s Artificial Intelligence Security Center (AISC) recommends integrating AI-powered security protocols early in the adoption process. According to IBM, the average cost of a data breach in the United States reached $9.36 million in 2024, with 95% of breaches motivated by financial gain. Institutions that employed AI-based security tools reportedly saved an average of $2.2 million per breach—underscoring the value of proactive AI implementation in cybersecurity.

Conclusion

AI offers transformative potential for financial institutions, particularly in loan underwriting. From enhanced decision-making and fraud detection to improved client service and operational efficiency, AI allows lenders to modernize their workflows while minimizing risk. With careful planning, secure implementation, and ongoing evaluation, AI can be a powerful asset in the future of banking and credit services.