Fraud Detection

A commercial bank got the machine learning infrastructure they needed to detect and flag fraud transactions.

Banking
E-fraud
Fraud Detection

Background

Two main challenges banks are faced with when trying to detect fraudulent transactions are:

The need to gather historical data and an imbalance in the classification. This is because only a small percentage of customers have fraudulent transactions.

Research has shown that more than 50% of people affected by fraud recover less than 25 percent of fraud losses; demonstrating that fraud prevention is key. Hence, more banks are investing in new technologies towards fraud prevention.

This bank needed an ML-based technology that could prevent fraud by detecting and flagging fraudulent transactions. They needed a company who could build this at the scale and speed they needed. This was when they chose Voyance.

The Solution

With Voyance ML, this company was fitted with technology that was utilized to proactively track and prevent cases of fraud, money laundering & identity theft.

This technology was designed to alert their managers and security/compliance officers to suspicious activity and transactions that could be cases of fraud.

Previously, investigating such transactions were time consuming and often resulted in a lot of false positives.Voyance ML stopped this.

The Results

With the Automated Fraud detection systems provided by Voyance, false positives reduced as they began to explore the use of AI and Machine Learning, in the detecting and analysis of the actual transactions that need further attention.

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