AI On Z Fraud Detection

Repository for credit card fraud detection notebooks and ONNX models

AI & Machine LearningJupyter NotebookApache-2.0

Abstract

AI On Z Fraud Detection is an open-source AI & Machine Learning project. Repository for credit card fraud detection notebooks and ONNX models. This repository provides TensorFlow source code for building and training credit card fraud models using an LSTM and a GRU. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This repository provides TensorFlow source code for building and training credit card fraud models using an LSTM and a GRU.

The models included in this repository are multi-layer LSTM or GRU models that analyze time series data to predict whether a credit card transaction is fraudulent.

The models consist of a recurrent neural network (RNN) with 2 layers of long short-term memory (LSTM) or gated recurrent unit (GRU), 200 units in each layer, followed by a dense layer. There is one output, which is Fraud/Non-fraud. A sequence of 7 transactions is used as the input to model.

2. Objective

Repository for credit card fraud detection notebooks and ONNX models

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Jupyter Notebook

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/IBM/ai-on-z-fraud-detection.git
cd ai-on-z-fraud-detection

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by IBM and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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