Bank checks are still popular and widespread globally for financial transactions. Dealing with a high volume of paper cheques, banking organizations face a serious challenge to process and verify user written information manually. This means that there is a great risk for banks to lose millions as a result of counterfeit check fraud based on fraudulent identification. That’s why it’s critical to automate and speed up the process of counterfeit check identification to avoid check frauds. To prevent a global banking organization from such fraud, the team aimed to develop and implement an AI/ML solution that would spot fraudulent checks in real time and minimize the number of checks that had to be reviewed manually.
Our team worked hand in hand with the client virtually to provide a solution that significantly improved the client’s existing imaging and verification software. With our ML model, we could flag potential frauds among the millions of checks being processed every month. Moreover, adopting a neural network to parse a historical database of previously scanned checks, including fraudulent ones, allowed us to analyze scanned images of handwritten checks including variable elements such as payee, check number, account and routing numbers, signatures, etc. In addition to that, our AI experts taught the network to identify what was normative for good checks and what checks are anomalous.