Proactive Fraud Detection with Machine Learning

Introduction

This project aims to proactively detect fraudulent financial transactions using a machine learning model. The model analyzes a large dataset of financial transactions to identify key patterns and factors that indicate fraud. Based on these insights, this project provides actionable recommendations for updating a company's fraud prevention infrastructure, complete with a strategy for measuring the success of the implemented changes.

Table of Contents

• Project Objective
• Dataset
• Methodology
  • 1. Data Cleaning
  • 2. Feature Engineering
  • 3. Handling Class Imbalance (SMOTE)
  • 4. Model Training & Evaluation
• Key Findings
• Actionable Recommendations
• How to Measure Success
• How to Run this Project
• Results

Project Objective

The primary goal is to develop a machine learning model to distinguish between legitimate and fraudulent transactions. The insights from the model are then used to create a set of actionable, data-driven recommendations to enhance fraud prevention systems.

Dataset

The dataset used is a synthetic financial dataset from Kaggle that mimics real-world transaction data.

Features:

• step: Represents a unit of time in the real world. In this case, 1 step is 1 hour of time.
• type: The type of transaction (e.g., CASH_OUT, PAYMENT, TRANSFER).
• amount: The amount of the transaction in the local currency.
• nameOrig: The customer who initiated the transaction.
• oldbalanceOrg: The initial balance of the originator's account before the transaction.
• newbalanceOrig: The new balance of the originator's account after the transaction.
• nameDest: The customer who is the recipient of the transaction.
• oldbalanceDest: The initial balance of the recipient's account before the transaction.
• newbalanceDest: The new balance of the recipient's account after the transaction.
• isFraud: This is the target variable. It is 1 if the transaction is fraudulent and 0 otherwise.
• isFlaggedFraud: An indicator from the system that flags a transaction as suspicious.

Methodology

The project follows a structured machine learning workflow to ensure the model is robust and the findings are reliable.

1. Data Cleaning

• Handled Outliers: Extreme values in the amount column were capped using the Interquartile Range (IQR) method to prevent them from skewing the model. The transaction amounts were clipped to a range of $0.10 to $516,181.13.
• Removed Unnecessary Columns: The nameOrig and nameDest columns were dropped as they are unique identifiers and do not provide predictive value for a general fraud detection model.

2. Feature Engineering

• One-Hot Encoding: The categorical type column was converted into a numerical format using one-hot encoding. This created a new binary column for each transaction type, allowing the model to interpret this information.

3. Handling Class Imbalance (SMOTE)

• Problem: The dataset is highly imbalanced, with fraudulent transactions making up only 0.11% of the data. This can bias the model towards predicting non-fraudulent transactions.
• Solution: SMOTE (Synthetic Minority Oversampling Technique) was applied to the training data only. SMOTE creates synthetic fraudulent examples to balance the classes, allowing the model to learn the patterns of fraud more effectively without overfitting.

4. Model Training & Evaluation

• A Logistic Regression model was chosen as a baseline for its simplicity and interpretability.
• The model was trained on the balanced (SMOTE-adjusted) training data.
• Its performance was evaluated on the original, imbalanced test set to simulate real-world conditions.

Key Findings

The logistic regression model identified several key factors that strongly predict whether a transaction is fraudulent.

• Strongest Fraud Indicators (Positive Correlation):

  • type_TRANSFER: Transactions involving a transfer of funds.
  • type_CASH_OUT: Transactions involving a cash withdrawal.

• Strongest Legitimate Indicators (Negative Correlation):

  • type_PAYMENT: Routine payment transactions are highly unlikely to be fraudulent.

Actionable Recommendations

Based on the model's findings, the following prevention strategies are recommended:

1. Implement Mandatory 2FA for High-Risk Transactions: For all transactions of type TRANSFER and CASH_OUT, trigger a mandatory Two-Factor Authentication (2FA) step before the transaction is processed.
2. Create a Tiered Alert System: Since PAYMENT transactions are overwhelmingly safe, they can be processed with lower scrutiny. This allows fraud prevention resources to be focused on the high-risk TRANSFER and CASH_OUT transaction types, improving efficiency.

How to Measure Success

To determine if these new measures are effective, the following plan should be implemented:

• A/B Testing: Roll out the new 2FA requirement to a test group (50% of users) while the remaining control group (50%) continues with the current system.
• Key Metrics: Over a 3-month period, monitor:
  • The fraud rate in both groups.
  • The false positive rate (how many legitimate transactions are incorrectly flagged).
  • The transaction completion rate to ensure legitimate customers are not overly inconvenienced.
• Definition of Success: The new measures will be considered successful if the test group shows a statistically significant reduction in the fraud rate without a major negative impact on the transaction completion rate.

How to Run this Project

To reproduce this analysis, follow these steps:

1. Prerequisites Ensure you have Python 3 and the following libraries installed:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• imbalanced-learn

2. Installation You can install the required libraries using pip:

pip install pandas numpy scikit-learn matplotlib seaborn imbalanced-learn