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

Category Distribution
Demographic Distribution
Age Distribution
Fraud / Non Fraud
Non Fraud

Algorithm & Model Building

Feature Importance


Receiver Operating Characteristic
AUC - 0.94

Ingestion of the data from multiple data sources

Source 1: demogrpahic data
Source 2: expenses data
Source 3: fraud data

Model Results

Test Table
Probability Prediction on Few Test Samples
Non Fraud

Management Recommendations

1. Avoiding fraud transcations is the number one business goal.
2. BanksĀ 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.