Prevented customer frauds with 81% accuracy for a leading payments bank

TransOrg built an anomaly detection model for a leading bank based on account information data and transaction behavior of customers by generating an “anomaly score” to predict the likelihood of a customer being fraudulent.

Model Accuracy

Top 1.11% scorers capture 43% frauds and Top 10% scorers capture 81% frauds

Client Objectives

To detect fraudulent customers and predict frauds.

Approach

Output

Model accuracy: Top 1.11% scorers capture 43% frauds and Top 10% scorers capture 81% frauds.
A list of accounts with high anomaly score in every prediction cycle was shared with Client's Anti Fraud Unit for verification.
A scheduler was created in order to give “anomaly score” at regular intervals.
Model performance tracker was also created and retraining was automated, in the case accuracy drops down a certain threshold.