Banks and other financial institutions (FIs) have been on high alert as they try to protect against losses brought on by AI-enabled fraudsters. Whether it’s account takeovers, check fraud, identity fraud, crypto scams, etc., FIs recognize their need to fight fire with fire and arm themselves with equally sophisticated AI-based tools that can improve and speed fraud detection and prevention—at scale.
This is where WorkFusion’s AI Agent for Fraud Alert Reviews is taking center stage at banks and FIs of all sizes. Named Isaac, “he” (the AI Agent) leverages and expands upon his historic capabilities in AML Transaction Monitoring to transform the fight against fraud. Isaac’s focus on fraud enables institutions to transform their slow, inefficient and inconsistent fraud alert review process into a rapid, highly efficient and consistent weapon that detects fraud faster and protects firms against unnecessary risk and losses. For a quick overview about Isaac—in his own words—watch his 1-minute video here.
Here’s how Isaac for fraud alerts works
Isaac performs five major steps to ensure your institution has “all the facts” needed to both streamline the fraud alert review process and ensure that nothing gets missed.
Steps 1 & 2: Alert collection and aggregation from multiple systems
Isaac integrates with existing fraud detection engines and other internal systems, and third-party data sources—including case management platforms, transaction monitoring systems, card network and authentication services, data and analytics tools, digital identity and behavioral intelligence solutions, and providers such as LexisNexis/ThreatMetrix and Refinitiv World-Check—to aggregate relevant context for each alert.
Step 3: Data and transaction analysis
Isaac leverages a slew of parameter-based rules, thresholds and decision trees to quickly determine the level of fraud risk associated with each individual transaction as well as potentially associated transactions. When Isaac spots a potentially fraudulent transaction or data that indicates fraud, he does not hesitate and instantly analyzes it and presents it to a human expert for review and approval.
Step 4: Narrative report generation
Isaac summarizes, recommends and authors data analysis that includes the complete set of AI results in a comprehensive narrative report which “he” shares via a Word document or other format preferred by the user.
Step 5: Routing to investigators for HITL review
Once Isaac completes the narrative report, he then distributes it to investigators—typically via the case management system, a shared drive, task distribution software, or any other means chosen by the organization.
Case example: Isaac protects against first-party fraud and account takeovers at a large bank/insurer
First-party fraud and account takeovers (ATOs) are two of the most popular forms of fraud plaguing the financial services industry in 2026. A large financial institution decided that enough was enough and purchased the Isaac AI Agent to expedite the review process for potential fraudulent transactions generated by several systems, including their behavioral intelligence system and two third-party systems. AI is a powerful tool when given enough data from several systems, because the AI itself can identify contextual and transactional anomalies, such as:
-
- Unlikely geographic data: A prime example of this is when logins for the same person happen in different cities or countries within a brief period of time.
-
- Sudden changes in account activity volume: This happens when an ATO occurs and the fraudster attempts to complete as many transactions as possible before getting discovered. This often leads to another anomaly—performing transactions in the middle of the night.
-
- Rapid account profile changes: A telltale sign of a fraudster compromising an account is detected when profile information for the user (e.g., password, phone number, backup email address, etc.) changes and the change is quickly followed by requests for unusual transactions not typically occurring in the account.
Collecting and aggregating the alerts data from the three systems, then applying his own AI intelligence, including lessons learned from machine learning history, Isaac quickly makes recommendations for human review. When the correct answer is not clear to Isaac, he escalates the case to a human expert—a feature built into Isaac’s typical workflow.
The results are astounding. Not only can the institution’s fraud analysts reduce their manual research time by over 70 percent, but they can also handle 2-3 times the volume of cases over their traditional efforts.
The institution’s management team sees Isaac as a robust means of protecting the business from significant losses. Not only does Isaac speed up investigations, but everyone knows the results of investigations are more reliable. Isaac never tires, always follows the same, consistent process, and is sure to report clearly and completely each time.
When it comes to protecting a financial institution from fraud, Isaac delivers multiple benefits. Click here for a demo of Isaac for Fraud Alert Revie? https://www.workfusion.com/ai-agents/isaac-ai-agent-for-fraud-investigations/
