On a related topic, the industry has seen a recent spike in check fraud for financial institutions which deploy legacy fraud prevention techniques. To help solve this problem, FI's can leverage image analysis and transaction analysis improvements coupled with self learning capabilities.
The underlying image analysis technologies include:
- Check stock validation (CSV) for counterfeit detection
- Automated signature verification (ASV) for forgery identification
- CAR/LAR discrepancy and payee name verification (PNV) for alteration detection
Learn Why Image Analysis is Used in Modernized Check Fraud Detection.
In order for these image analysis technologies to be effectively deployed, self learning enables the system to adapt to the image and transactional characteristics of each account, storing these behaviors along with image snippets for real-time fraud detection capabilities. This self learning approach is based on thresholding rules, flagging major variations in check stock and signatures which may indicate a potential fraudulent payment. Small variations are automatically added to each account profile while larger variations require operator review. Once reviewed, feedback is provided back to the system to validate a “good” or “bad” payment. These account profiles become mature based on the learning history of the transactions. The result is a fraud detection process which identifies a high degree of fraudulent items with minimal suspect levels (False positives).
OrboGraph continues to invest in intelligent payment automation technologies for check processing including AI, deep learning and self learning within the OrboAnywhere suite. It is our quest to drive straight-through processing for the industry.