Checks remain the payment method most frequently impacted by fraud, and the costs of check fraud carry far beyond stolen finances. Fraud mitigation and prevention can be difficult to quantify, but the impact is substantial on every institution.
Consider the hours of labor required to review and investigate checks flagged as potentially suspicious. This process is becoming more difficult as fraudsters use AI and other advanced technologies to create increasingly convincing alterations and counterfeits. Community bank fraud mitigation teams are tasked with identifying threats with spiking volumes, false positives and data coming from multiple systems.
Manual processes and visual inspection have reached their limits as fraudulent items are harder to distinguish and decisions need to be made quickly.
The scale of payments fraud adds to the pressure. According to the 2026 AFP Payments Fraud and Control Survey, 76% of U.S. organizations experienced payments fraud activity in 2025. Yet just 17% of organizations leverage AI to combat it.
AI-powered document analysis can automate much of the review process while directing staff attention to the items that warrant closer investigation. Rather than relying primarily on visual inspection or treating every exception equally, AI should be used to analyze handwriting, signatures, check stock, endorsements and other characteristics, simultaneously identifying irregularities that may signal an altered or counterfeit check.
This analysis happens within existing check-processing workflows and at scale, helping banks review significantly more information without requiring employees to manually inspect every item. Confidence scoring prioritizes potentially suspicious checks based on risk, while explainable outputs provide insight into why an item was flagged. Investigators move through routine reviews more efficiently while maintaining human oversight for decisions that require additional judgment.
The operational impact can be significant. One financial institution with less than $10 billion in assets was experiencing more than $70,000 in monthly fraud losses while an eight-person investigations team reviewed 200 to 300 potentially suspicious items each day. High alert volumes, false positives and fragmented review processes placed additional pressure on an already lean team.
By introducing AI-powered document analysis into the review process, the institution reduced alert fatigue and identified altered and suspicious checks more efficiently. Automation narrowed the field of items requiring investigation, allowing employees to devote more attention to higher-risk activity.
Reducing unnecessary manual review helps community bank employees resolve legitimate transactions faster and spend more time supporting customers. They are then free to focus on personal support and other priorities. This allows community banks to build on their strengths and differentiators, supporting the community and helping Main Street America thrive.
AI does not replace human judgment in fraud operations. Instead, automating repetitive analysis gives investigators better information. Community banks can use that judgment more effectively, strengthening fraud prevention while giving their teams more time to focus on the customers and communities they serve.