Why Explainable AI is Critical for Fraud Defense
- Fraud will evolve through synthetic identities, deepfakes, mules, takeovers, and emotional scams
- Explainable AI reveals why fraud models flag transactions
- Banks need XAI to meet regulations, prove fair automated decisions, and continually refine fraud defenses
How large of a challenge was fraud in 2025? Well, according to the Gasa Global State of Scams 2025 Report, global fraud losses reached an estimated $442 billion, with nearly 70% of adults worldwide encountered a scam in 2025. With losses this massive, there is really no excuse for any individual or organization to ignore the problem.
With this in mind, WorldLine Financial Service has posted a new article on the five fraud trends to watch for in 2026 and beyond:
- Synthetic identities
- Deepfakes and real-time scams
- First-party and money mule fraud
- Account takeover
- Emotional scams
So, what tools are available to address the fraud problems?
Understanding Explainable AI (XAI)
Explainable AI (XAI) refers to methods and techniques that help humans understand, trust, and interpret the decisions and predictions made by machine learning models, making "black box" algorithms transparent by revealing the reasoning, data, and potential biases behind their outputs.
Why Is XAI Important for Fraud Detection?
For fraud detection -- particularly check fraud, this means that a fraud analyst is providing reasoning behind the decision as to why the system flagged an item, transaction, or account for possible fraud.
XAI works through techniques that approximate complex models with simpler, human‑readable explanations around individual predictions. Methods such as LIME and SHAP assign importance scores to features, making it clear how factors like income, transaction history, or geography contributed to a specific decision. These methods can provide both global insight into how a model behaves overall and local explanations for single cases.
As an example, a deposited check is flagged by the fraud system. With little detail, you are forced to spend precious time examining the entire check and transaction data. Now, with XAI, fraud analysts are informed why the system flagged the check item and focus on that particular area to make the final decision.
This transparency is not only crucial for trust, but also demanded by regulators and financial institutions.
Leveraging XAI in Check Fraud Detection
We've already covered the importance of XAI, as it provides clarity for fraud analysts and helps meet new regulatory demands. For check fraud detection, it's an important component in a multi-layered technology approach.
For image forensics, fraud analysts are able to review a flagged item and isolate the area(s) of the check image to compare against previously cleared items. For instance, before XAI, when a check was identified as counterfeit, analysts were required to examine the whole check image and compare it with previously cleared checks. Now, XAI provides further details, such as missing security symbols, so analysts can focus on a particular area to determine if the check is fraudulent or not.
For deposited items, XAI serves as another input in understanding and mitigating risks associated with financial transactions by importing indicators and data attributes connected to individual accounts. This approach enables financial institutions to score deposits by utilizing information such as account risk scores and status indicators.
OrboGraph has deployed Explainable AI in our OrboAnywhere Turbo 6.0 release. To learn more, visit www.orbograph.com/orboanywhere-turbo-6-0.
