The task of fraud detection often boils down to outlier detection, in which a dataset is verified to find potential anomalies in the data. Traditionally, this task was deemed a manual task, where risk experts checked all transactions manually. Even though there is a technical layer, it is purely based on a rules base that scans through each transaction, and then those shortlisted as suspicious are sent through for a manual review to make a final decision on the transaction. However, there are some major drawbacks to this system:
- Organizations need substantial fraud management budgets for manual review staff.
- Extensive training is required to train the employees working as manual review staff.
- Training the personnel to manually review transactions is time consuming and expensive.
- Even the most highly trained manual review staff carry...
