Banking Case Study: Fraud Detection
Dashboard presents results of our model regressor which predicts whether a customer transaction is fraudlent in a banking sector.
Following ingestion from multiple sources, we explore multi-characteristics of the data, build our model and finally present the results.
Characteristics of the data exhibiiting multi-characteristic distributions
Algorithm & Model Building
Ingestion of the data from multiple data sources
1. Avoiding fraud transcations is the number one business goal.
2. Banks need to predict which transactions are fraudlent to avoid costs.
3. We recommend the bank to consider Features Importance chart which are strong indicators to pre-empt fraud.