fraud-transaction-detection

Fraud transaction detection using Machine Learning algorithms on highly imbalanced dataset using ANN, Random Forest Classifier and XGBoost Classifier

Observations

• The dataset is highly imbalanced, with only 0.129% of observations being fraudulent.
• There is no missing data in the dataset
• The dataset consists of 11 features which needed to be transformed

Checking for multi-collinearity

Summary and Explanation

• oldbalanceOrg and newbalanceOrg are perfectly correlated because these two columns represent the original and new balances in the sender's account after the transaction.
• oldbalanceDest and newbalanceDest are also perfectly correlated because these two columns represent the original and new balances in the recipient's account
• nameOrig and nameDest are mass categorical variable

Action

1. Removing newbalanceOrig and newbalanceDest to avoid multicollinearity
2. Removing nameOrig and nameDest because of irrelavnce

Weight of the balanced dataset

After applying Undersampling and then Oversampling the following are the weights of the new dataset :

• Fraudulant transaction weight: 0.3335339444434781
• Non-Fraudulant transaction weight: 0.6664660555565219

ANN Loss Chart

ANN Performance

Random Forest Performance

XGBoost Performance

Model Performance Comparison

ANN_model (Artificial Neural Network):

• F1-score on the training set: 0.9500
• F1-score on the test set: 0.9493

Random Forest:

• F1-score on the training set: 1.0 (perfect score)
• F1-score on the test set: 0.9992

XGBoost:

• F1-score on the training set: 0.9967
• F1-score on the test set: 0.9963

Conclusion

Random Forest Model works best