Key takeaways
- H2O Driverless AI builds check fraud models that combine features from the check image with account, deposit and payee history.
- Each check receives a risk score and reason codes, and your policy decides which scores clear, which hold and which go to an investigator.
- Investigator decisions feed back into the training data, so the model learns from the fraud your team confirms.
- The scoring layer sits beside your existing item processing and fraud systems.
Check fraud still lands in a manual queue
Checks keep arriving through branches, ATMs and mobile deposit, and each channel gives fraudsters a way in. Counterfeit stock, washed and rewritten payees, and forged signatures all look ordinary at a glance.
Fixed rules on amount and account age flag many good items and still miss a well-made fake. Your reviewers spend the day on checks that turn out to be fine, while the loss sits in the items nobody looked at. Every hold also touches a customer: a deposit stopped without reason costs trust.