use-case

Credit Card Fraud Detection

Objective

Occurrence of fraudulent transactions not only results in high expense for the bank but also puts the company’s brand identity in a bad light. That’s why, predicting and avoiding such transactions is an unavoidable necessity for the banks. But, at the same time, investigating each and every transaction is not a viable option either as it will lead to a much higher cost for the bank as well as an unwanted and not so delightful experience for its genuine customers. Effectively predicting fraudulent transactions, before they actually happen, remains a big challenge for companies in this industry.

Our Solution

A number of Machine Learning models effectively predict, identify and red-flag the transactions which are expected to be fraudulent given the feature characteristics of the transaction. With this, the fraud detection team at your bank will need to investigate only those transactions which indicate a high probability of being a fraud. This reduces the loss from fraudulent transactions without poorly impacting the customer experience as only a few high probability transactions will need to be investigated.

Benefits

Predictive technology using the most advanced methods in the field of AI and ML yield an efficient and accurate rank of transactions expected to be fraudulent. The neural network technologies more closely replicate the way people think. Unlike linear methods, neural networks can identify complex use patterns and automatically adapt and learn to adjust the model outcome.This technology works two-fold: captures most of the fraudulent transactions and, at the same time, reduces the number of correct transactions which need to be investigated.

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