Real-time Fraud Detection using Deep Learning
Traditional fraud protection methods in the Fintech industry have been rule-based, where a human defines the rules. However, this is not scalable due to latency and with new fraud patterns emerging every day. Also, merely classifying transactions as anomaly is not enough as we want to gain a better insight into the root cause(s) of the anomaly.
To solve this, we can use deep learning techniques to reconstruct normal transactions to find anomalies from the normal patterns in the transactions. On detection of an anomaly, our model traces the most influential variable(s) that caused this anomaly.
In this session, we will give a brief overview of deep learning, anomaly detection and how to trace the root cause(s) of the anomaly.
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