Online-Payment-Fraud-Detection

Introduction

Online payment is the most popular transaction method in the world today. However, with the increase in online payments, there has also been a rise in payment fraud. Fraudulent transactions can cause significant financial losses, making fraud detection a critical challenge in the financial sector. The objective of this project is to identify fraudulent and non-fraudulent payments using machine learning techniques.

Dataset

The dataset is sourced from Kaggle and contains historical information about online transactions. This data can be used to develop models capable of detecting fraudulent transactions.

Features:

• step: Represents a unit of time where 1 step equals 1 hour.
• type: Type of online transaction (e.g., CASH-IN, CASH-OUT, DEBIT, PAYMENT, TRANSFER).
• amount: The amount of the transaction.
• nameOrig: Customer initiating the transaction.
• oldbalanceOrg: Balance before the transaction.
• newbalanceOrig: Balance after the transaction.
• nameDest: Recipient of the transaction.
• oldbalanceDest: Initial balance of the recipient before the transaction.
• newbalanceDest: New balance of the recipient after the transaction.
• isFraud: Indicates whether the transaction was fraudulent (1) or not (0).

Methodology

To detect fraudulent transactions, we will use various machine learning techniques, including:

• Exploratory Data Analysis (EDA): To understand patterns and relationships within the dataset.
• Feature Engineering: Creating new features and handling missing values.
• Machine Learning Models:
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Gradient Boosting (XGBoost, LightGBM)
  • Neural Networks

Results

• Model performance will be evaluated using Accuracy, Precision, Recall, and F1-score.
• Confusion Matrix will be used to visualize fraud detection.

Future Work

• Implement deep learning techniques such as LSTMs.
• Use real-time fraud detection techniques with streaming data.
• Improve model interpretability using SHAP or LIME.

Conclusion

This project aims to build an effective fraud detection system using machine learning. By analyzing transaction data, we can identify patterns and improve fraud detection accuracy.

Acknowledgments

• Kaggle for providing the dataset.
• Open-source ML communities for guidance and support.