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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. Quale Infotech achieved this using its Optical Character Recognition (OCR) and deep learning technology to scan checks, process data, and verify signatures. Our AI 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.

Challenges Addressed

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Manually intensive check review
Manual Signature verification
High volume of verifications
Highly analytical in nature
System unavailability           

The Outcomes

50 %

Reduction in fraudulent transactions.

5 Million $

Annual savings on fraud losses.

Reduction in operating costs of manual check validation.

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