summer-of-code-2024

Week 1: Fraud Detection System

Table of Contents

1. Introduction
2. Why ML for Fraud Detection?
3. Workflow Overview
4. Detailed Task Breakdown
   • 4.1. Set Up Google Colab
   • 4.2. Find and Load a Dataset
   • 4.3. Data Preprocessing
   • 4.4. Feature Engineering
   • 4.5. Address Class Imbalance
   • 4.6. Implement Classification Algorithms
   • 4.7. Model Evaluation
   • 4.8. Model Interpretability
   • 4.9. Create a Real-time Fraud Detection API
5. Deliverables
6. Submission Guidelines
7. Resources
8. FAQ

1. Introduction

Welcome to Week 1 of the AI/ML Development Track. This week, you’ll build a fraud detection system for our Point of Sale (PoS) application. You’ll use Google Colab for development, find a suitable dataset, and implement various machine learning techniques to identify potentially fraudulent transactions.

2. Why ML for Fraud Detection?

Traditionally, businesses relied on rules alone to block fraudulent payments. Today, rules are still an important part of the anti-fraud toolkit, but using them on their own also caused some issues.

False positives: Using lots of rules tends to result in a high number of false positives - meaning you’re likely to block a lot of genuine customers. For example, high-value orders and orders from high-risk locations are more likely to be fraudulent. But if you enable a rule which blocks all transactions over $500 or every payment from a risky region, you’ll lose out on lots of genuine customers’ business too.

Fixed outcomes: The thresholds for fraudulent behavior can change over time - if your prices change, the average order value can go up, meaning that orders over $500 become the norm, and so rules can become invalid. Rules are also based on absolute yes/no answers, so don’t allow you to adjust the outcome or judge where a payment sits on the risk scale.

Inefficient and hard to scale: Using a rules-only approach means that your library must keep expanding as fraud evolves. This makes the system slower and puts a heavy maintenance burden on your fraud analyst team, demanding increasing numbers of manual reviews. Fraudsters are always working on smarter, faster, and more stealthy ways to commit fraud online. Today, criminals use sophisticated methods to steal enhanced customer data and impersonate genuine customers, making it even more difficult for rules based on typical fraud accounts to detect this kind of behavior.

Machine learning can often be more effective than humans at uncovering non-intuitive patterns or subtle trends which might only be obvious to a fraud analyst much later. Machine learning models are able to learn from patterns of normal behavior. They are very fast to adapt to changes in that normal behavior and can quickly identify patterns of fraudulent transactions.

3. Workflow Overview

1. Set up Google Colab with GPU support
2. Find and load a fraud detection dataset
3. Develop a machine learning pipeline
4. Preprocess data and engineer features
5. Address class imbalance
6. Implement and compare classification algorithms
7. Evaluate model performance
8. Interpret model decisions
9. (Optional) Create a real-time fraud detection API

4. Detailed Task Breakdown

4.1 Set Up Google Colab

• Access Google Colab and create a new notebook with GPU support.
• Google Colab Quick Start Guide

4.2 Find and Load a Dataset

• Search for a fraud detection dataset on Kaggle or similar platforms.
• Use pandas to load and initially explore the dataset.
• Kaggle Datasets
• UCI Machine Learning Repository

4.3 Data Preprocessing

• Handle missing values, encode categorical variables, and normalize numerical features.
• Pandas Data Cleaning Tutorial
• Scikit-learn Preprocessing Guide

4.4 Feature Engineering

• Try creating new features to improve model performance.
• Consider using automated feature engineering tools.
• Featuretools Documentation
• Feature Engineering Techniques Article

4.5 Address Class Imbalance

• Apply techniques like SMOTE or random under-sampling to balance the dataset.
• Imbalanced-learn Documentation

4.6 Implement Classification Algorithms

Implement and compare multiple algorithms. These are some common Classification (classifying data into fraud or non-fraud categories) models:

• Logistic Regression: Logistic Regression Tutorial
• Random Forest: Random Forest Tutorial
• XGBoost: XGBoost Tutorial
• LightGBM: LightGBM Tutorial
• Support Vector Machines: SVM Tutorial
• Neural Networks: Keras Tutorial

4.7 Model Evaluation

• Use appropriate metrics like precision-recall curve and ROC AUC score.
• Scikit-learn Model Evaluation

4.8 Model Interpretability

• (Optional) Apply SHAP values to understand feature importance and model decisions.
• SHAP in Python Tutorial

4.9 Create a Real-time Fraud Detection API

• (Optional) Use FastAPI to create an API for real-time fraud detection.
• FastAPI for ML Tutorial

5. Deliverables

1. Google Colab notebook with your entire fraud detection pipeline
2. A very small Markdown report discussing your approach, challenges, and results
3. (Optional) Python script for the fraud detection API

6. Submission Guidelines

1. Share your Google Colab notebook as a link, or ipynb file
2. Submit your report as a md file
3. (Optional) Submit your API as a py file

7. Resources

• Machine Learning Crash Course
• Hands-On Machine Learning with Scikit-Learn and TensorFlow
• Fast.ai Practical Deep Learning for Coders
• Kaggle Learn Machine Learning

8. FAQ

Q: How do I choose between different algorithms? A: Start with simpler models (e.g., Logistic Regression) and progressively try more complex ones, comparing their performance.

Q: Is it necessary to complete all optional tasks? A: No, focus on core tasks first. Optional tasks are for those who finish early or want extra challenges.