Fraud detection has always been a challenge for banks and financial institutions around the world. Today, given the fact that the check-processing times have been drastically reduced thanks to electronic payments and secured transactions via automated clearinghouse (ACH), banking and commerce industry still feel the heat devoting their valuable time and money to manually verify thousands of handwritten checks.
Fraud management has been a time-consuming and expensive activity for banking and financial institutions globally, thanks to the number of transactions have increased exponentially due to a plethora of payment channels available today – smartphones, kiosks, credit/debit cards. Not to mention, criminals have become adept at finding loopholes in the system.
As a result of check fraud by counterfeiters, banking and commerce industry risk losing millions as it is getting increasingly tough for businesses to authenticate financial transactions. Therefore, it is critical not only to identify counterfeit transactions but also the financial scammers.
To reduce such occurrences of check fraud or fraud-related losses and damages and help reveal scams, a real-time assessment with the help of machine learning (ML) and predictive analytics will predict the probability of fraud. A global bank partnered with Eclature Technologies to build a robust solution based on AI (artificial intelligence) and machine learning to help speed up the check verification process and at the same time lower cost of operation in the wake of adopting such a niche technology.
To meet The Client’s goals, we needed to identify fraudulent checks in real-time and over and above this, reduce the number of checks requiring manual review. The Client already has the mechanism of using OCR (optical character recognition) and deep learning technology (DLT) to scan customer checks, process data and verify signatures. We based our model on an open-source software library called Google TensorFlow™ which essentially uses a neural network to skim through the historical database of previously scanned checks, including the ones that are known to be potentially fraudulent.
In order to distinguish good checks from anomalous ones, our team trained the neural network to use a set of comparative algorithms to predict fraud threats. By automatically evaluating various factors on scans of deposited checks to those in the database, our model potentially signals possible fraud scenarios in real-time. For this type of problem, AI and machine learning (ML) shines as a unique solution as it is a critical part of the fraud detection toolkit.
Besides, the application assigns a confidence score to each scanned check, signalling it as good, fraudulent, or needs further process of review. We made the solution to be as scalable and configurable as possible to the client’s evolving needs. The Client was not only able to detect fraud earlier but also reduced reputation, improve profitability and lower financial loss.
Machine Learning Solution Reduce the Incidence of Check Fraud
The Eclature AI advantage
Fraud is ever present in financial services and scammers are constantly developing new ways and techniques to perpetuate it.
Eclature’s AI-powered machine learning solution (with predictive analytics) operates with near human intelligence. The solution automatically performs signature and image analysis in real-time whereby it determines transactions that are most likely to be fraudulent, counteracts the counterfeiters and reduce losses.
Every transaction the AI-enabled solution model processes increase its accuracy of detection by processing large amounts of data and adding up to its large volumes of historical information, so it’s continually learning the practices of habitual fraudsters to predict, prevent or defeat them.
- 55% reduction in fraudulent transactions
- $10 million annual savings on fraud losses
- Reduced operating costs manual check validation
- Response time less than 60 milliseconds, with up to 1,300 checks per second processed