Credit Card Fraud Detection Using Machine Learning

Objective

The primary goal of this project is to develop a machine learning model that can accurately detect fraudulent credit card transactions. Credit card fraud is a significant concern in the financial industry, leading to substantial monetary losses. This project aims to create a reliable detection framework that minimizes the occurrence of false positives while maximizing the identification of actual fraudulent transactions.

Project Workflow

1. Data Collection and Preprocessing

   • Use a publicly available dataset containing historical credit card transactions labeled as fraudulent or legitimate. The dataset typically includes features such as transaction amount, time, and anonymized details of each transaction.

   • Preprocessing steps include:

     • Handling missing values, if any.

     • Scaling numerical features to ensure uniformity (using techniques like normalization or standardization).

     • Encoding categorical variables (if present) to numerical formats.

     • Addressing class imbalance through techniques like undersampling, oversampling, or using specialized algorithms.

2. Exploratory Data Analysis (EDA)

   • Perform EDA to understand the data distribution and identify patterns or anomalies.

   • Visualize the data using histograms, box plots, and scatter plots to examine relationships between features.

   • Analyze the distribution of fraudulent vs. non-fraudulent transactions to understand the class imbalance.

3. Feature Engineering

   • Create new features that may provide additional insights, such as transaction amount relative to user behavior or time-based features.

   • Select important features based on statistical tests or feature importance measures from algorithms like Random Forest.

4. Model Selection

   • Choose appropriate machine learning algorithms for classification, such as:

     • Logistic Regression

     • Decision Trees

     • Random Forest

     • Support Vector Machine (SVM)

     • Gradient Boosting (e.g., XGBoost, LightGBM)

     • Neural Networks (for more complex patterns)

   • Train multiple models to compare performance metrics.

5. Model Training and Evaluation

   • Split the dataset into training and testing sets (e.g., 80% training, 20% testing).

   • Train the models using the training data and fine-tune hyperparameters for optimal performance.

   • Evaluate models on the testing set using metrics such as:

     • Precision: Measures the proportion of correctly identified fraudulent transactions.

     • Recall: Measures the ability to detect actual frauds.

     • F1-Score: Harmonic mean of precision and recall.

     • ROC-AUC Score: Measures the trade-off between true positives and false positives.

   • Select the model that provides the best balance of high precision and recall.

6. Deployment

   • Integrate the trained model into a real-time system for monitoring credit card transactions.

   • Use the model's output to trigger alerts for suspicious transactions, prompting manual review or automatic action.

   • Implement strategies for continuous monitoring and updating of the model to adapt to evolving fraud tactics.

7. Challenges and Mitigation Strategies

   • Class Imbalance: Since fraudulent transactions are rare, the dataset is highly imbalanced. Techniques like Synthetic Minority Over-sampling Technique (SMOTE) or adjusting the decision threshold can help mitigate this issue.

   • Concept Drift: Fraud patterns change over time, requiring the model to be updated regularly.

   • Data Privacy and Security: Handle sensitive data with caution to comply with data protection regulations.

Tools and Technologies

• Programming Languages: Python or R

• Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, TensorFlow/Keras (for neural networks)

• Visualization Tools: Power BI, Tableau, or Python visualization libraries (Matplotlib, Seaborn)

• Data Storage: SQL databases or cloud storage for large datasets

Expected Outcomes

• A machine learning model capable of detecting fraudulent credit card transactions with high accuracy.

• Reduced financial losses from fraud by improving the detection rate of fraudulent activities.

• An adaptable system that can evolve over time with changing fraud patterns.

Impact

This project aims to bolster the financial industry's efforts in combating credit card fraud, enhancing customer trust, and reducing the economic burden caused by fraudulent activities.

Content

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Muphulusi/Anamoly Detection.ipynb at main · 18069682/Muphulusi (github.com)