Combating Sophisticated Fraudsters Requires Sophisticated Fraud Detection and Mitigation Solutions

Check fraud continues to thrive in the financial services industry, remaining the subject of more fraud than any other payments type. According to ABA’s most recent Deposit Account Fraud Survey Report, fraud losses against deposit accounts total $2.8 billion annually, with forgeries, counterfeits, and alterations topping the list of types.

Financial institutions still reliant on legacy systems may struggle to keep pace with newer and more advanced fraud methods. It’s no secret that fraudsters are becoming more sophisticated, conducting repetitive small-deposit account transaction fraud attempts. This makes it increasingly difficult for financial institutions to detect and prevent fraudulent activity.

Thwarting these fraudulent attempts requires institutions to use solutions that start prevention at the point of disbursement. Additionally, these solutions should also leverage automated verification and fraud detection at all points in the clearing process. By investing in newer generation technology to detect fraud, institutions can create two quantifiable results – improved detection accuracy and optimized labor utilization.

Along with these quantifiable impacts when compared to legacy systems, robust solutions that successfully detect and mitigate check fraud leverage four functional areas:

1. Transaction Analysis – Processes debits and credits from deposits and withdrawals, detecting suspicious items like unusual check numbers, amounts, or duplicate check numbers. It applies various tests at both the account and entity levels, evaluating factors such as account velocity, transaction volume, and abnormal deposit or withdrawal amounts.
2. Check Stock Validation – Analyzes presented check images against historical, referenced check images, validating the consistency and accuracy of check stock. This effectively identifies counterfeit inclearing and over-the-counter checks faster and with increased accuracy and reliability than visual inspection.
3. Signature Verification – Employs machine learning algorithms and advanced decision options to provide a detailed analysis of check signatures, enabling efficient evaluation of suspicious inclearing and over-the-counter checks. Additionally, it enhances confidence in acceptance and return decisions by comparing digitized signatures to reference images, accommodating multiple signatories on the same account, and monitoring checks that require dual signatures.
4. Alteration Detection – Leverages artificial intelligence (AI) “deep learning” models to compare check attributes and features against each other to identify checks that have potentially been washed or have altered fields. Alteration detection analyzes handwriting by identifying individual writers by the style and traits of their handwriting. Training AI models on larger, diverse datasets of handwriting samples allows them to learn and recognize a wide range of handwriting styles.

Although check fraud remains an issue that financial institutions of all sizes must contend with, institutions that are properly prepared for sophisticated fraudsters can mitigate and eliminate check fraud, protecting themselves and their customers.

For more information download our interview: Summarizing the breadth and depth of ARGO’s OASIS Fraud solution