Increase the percentage of fraudulent checks identified by system in real-time

The Process

 

Even with lower check-processing times due to electronic payments and automated clearing house (ACH) transactions, banks must still manually verify millions of handwritten checks. Annually, banks risk losing millions as a result of check fraud by counterfeiters. Because a percentage of the funds is made readily available to the depositors, it’s critical to identify counterfeit checks quickly. To reduce the incidence of check fraud, a global bank partnered with us to build a solution based on Artificial Intelligence (AI) machine learning to speed up check verification.

 

The Solution

 

The solution needed to identify fraudulent checks in real-time, as well as reduce the number of checks requiring manual review. Aiwozo achieved this using its Optical Character Recognition (OCR) and deep learning technology to scan checks, process data, and verify signatures. Aiwozo’s model, based on Google TensorFlow™, uses a neural network to parse a historical database of previously scanned checks, including those known to be fraudulent. We trained the neural network to use a set of comparative algorithms to distinguish proper checks from anomalous ones. By automatically comparing various factors on scans of deposited checks to those in the database.

The Outcomes

50% reduction in fraudulent transactions.

$5 million annual savings on fraud losses.

Lower manual check validation operating costs.

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Challenges Addressed

Manually intensive check review process
Manually Intensive Process
Signature verification
Signature verification
High volume
High volume
Highly analytical in nature
Highly analytical in nature
System unavailability
Multiple systems for different insurance products

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