Check fraud is still a daily reality for financial institutions. The Association for Financial Professionals’ 2026 AFP Payments Fraud and Control Survey found that more than half (58%) of organizations reported check fraud last year, making checks the most frequently impacted payment method in the survey. Despite the risks, vendor requirements force many businesses to stick with paper checks, perpetuating a cycle of high fraud exposure.
It is important that bankers view check fraud as a real and persistent threat, not just a legacy operational issue. However, many banks and credit unions still depend on legacy fraud controls like manual review, visual inspection and static rules developed for branch-centric environments. Those controls struggle when checks process through remote deposit capture, ATMs and image exchange networks. When fraud is detected late, exception queues grow, analysts are pulled into investigations and commercial clients may face delays on legitimate deposits.
Artificial intelligence (AI) is starting to change how financial institutions approach this challenge. In the same way that AI has reshaped card and digital payment fraud detection, institutions are now using machine learning and pattern recognition on check images and related data to flag risk earlier in the process. Models can evaluate signatures, handwriting, printed text, check layouts and stock characteristics at scale, and they are able to detect inconsistencies or alterations that are difficult for human reviewers to see. Combined with account history and transaction behavior, these signals support a layered view of risk.
For technology and risk leaders, three questions can guide modernization:
- Which signals should drive check fraud decisions?
Many systems still lean heavily on transaction patterns and account history. AI‑based approaches can add richer inputs by analyzing signature behavior over time, handwriting/payee name consistency, check stock, layout and alterations between written and numeric amounts. Bankers can then clarify which document and behavioral signals matter most for their risk profile by segment, channel or check type. This helps leadership evaluate vendors and avoid signal gaps and unnecessary complexity. - How explainable and governable should decisions be?
As model risk expectations evolve, boards and regulators expect financial institutions to understand how models operate, which features drive outcomes and how thresholds are monitored. Technology leaders should consider whether staff should see why an item was flagged, such as a document anomaly, a behavioral pattern, transaction context or some combination. They should also ask how models are validated and recalibrated as fraud patterns change, and what audit trails exist when a decision involves a hold, return or exception on an important business deposit. Transparent risk scoring and comprehensive reporting make it easier to satisfy governance expectations while maintaining confidence in automated decisions. - How will new tools fit into existing workflows?
Even accurate models can add friction if they sit outside day‑to‑day operations. Effective deployments integrate with item processing platforms and remote deposit capture systems, then route alerts and scores into existing back‑office queues and case‑management tools so teams do not have to shift between multiple environments. The objective is to reduce manual review and rework, not relocate it. That is where AI‑driven image and handwriting analysis can align with existing fraud operations by handling routine, repeatable checks and surfacing only the items that truly require human judgment.
Partnership structure completes the picture. When banks and credit unions work with fintech providers on check fraud controls, they enter a long-term operating relationship. Successful programs often start with a specific use case, such as high‑value business checks or a single channel, map current workflows and agree on success metrics that include loss performance, manual review volumes and decision times. Regular performance reviews and clear processes for updating models or rules help both sides keep pace with changing fraud patterns.
Modern check fraud solutions can deliver earlier detection at the point of presentment, more consistent evaluation across channels, deeper use of document and behavioral signals alongside transaction data and a lower operational burden on fraud teams. Financial institutions that approach check fraud in this way, as a strategic technology decision supported by AI and the right partnerships, may be better positioned to keep pace with evolving schemes while keeping complexity manageable.
Previous