Fraud Detection using Graph Machine learning

This repository consists of a solution that includes the analysis of financial data and detection of Fraud detection using Graph Machine Learning. The solution uses a Relational Graph Convolutional Network that generates unique graph features from neighbourhood information to aid in better and effective detection of fraud.

The setps are as follows:

1. Pre-processing
2. Making edgelists using the user identity columns
3. Generating a multi dimensional heterogenous graph using the data along with these edgelists
4. Using the Deep Learning models to generate predictions

The dataset used was the IEEE-CIS Fraud Detection Dataset provided by Vesta on Kaggle

Two RGCN (Relational Graph Convolutional Networks) were developed and tested

1. Shallow RGCN
2. Deep RGCN

Despite the heavy class imbalance, the Deep RGCN produced great results and outperformed the shallow RGCN. The evaluation metric scores were as follows:

• F1: 0.6228 (Shallow network 0.48)
• Accuracy: 98% (Shallow network 97.48%)
• Precision: 0.8872 (Shallow network 0.8240)
• Recall: 0.4798 (Shallow network 0.3410)
• Confusion matrix:

	Predicte Positive	Predicted Negative
Positive	1950	248
Negative	2114	113796

The architecture of the solution is as follows:

To run the code, simply run the Jupyter notebooks in this order:

1. DataPrep
2. Modelling
3. Visualization

References

Detecting fraud in heterogeneous networks using Amazon SageMaker and Deep Graph Library