Industry: Finance – Fraud Detection in Digital Transactions
Case Study: AI-Powered Fraud Detection in Financial Transactions
Fraud in financial transactions leads to billions of dollars in losses globally. Traditional rule-based fraud detection struggles to keep up with evolving fraudulent techniques. Machine learning models can detect complex patterns in real-time transactions, reducing financial losses, minimizing false positives, and improving security.
- Class imbalance: Fraudulent transactions are rare (~0.1% of all transactions).
- False positives impact: Too many false alerts frustrate legitimate users.
- Evolving fraud patterns require adaptive models.
- Feature engineering to identify new behavioral patterns.
- Accurately classify fraudulent transactions while minimizing false positives.
- Optimize precision-recall balance for business-friendly fraud detection.
- Develop a real-time fraud detection pipeline with minimal latency.
- Provide interpretable insights for financial analysts.
- Model Types:
- Supervised: Naïve Bayes, Random Forest, XGBoost
- Unsupervised: PCA, K-means, DBScan
- Performance Metrics: Accuracy, Precision, Recall, F1-score, AUC-ROC, MCC, Jaccard Score, Balanced Accuracy
- Feature Importance: Identifying top features contributing to fraud detection
- Used Power BI, Pandas, and Seaborn for data exploration and visualization.
- Preprocessing with Scikit-learn and Pandas for standardization, encoding, and balancing.
- Introduced a custom feature
isValidTransactionto flag inconsistencies in transactions. - Implemented a Gaussian Naïve Bayes model within an optimized sklearn pipeline for seamless feature transformation and classification.
- Removed self-transactions where sender and receiver are identical to reduce misleading patterns.
- Balanced dataset to improve recall and overall fraud detection rates.
- Data cleaning and preprocessing including handling missing values, normalization, encoding, and balancing.
- Key Performance Indicators (KPIs): Accuracy, Precision, Recall, F1 Score, ROC AUC Score, Log Loss, Jaccard Coefficient, Matthews Correlation Coefficient (MCC), Balanced Accuracy.
- Visualizations include fraud heatmaps, correlation plots, anomaly detection, precision-recall and ROC curves.
- Best model: Naïve Bayes with balanced test accuracy.
- Key features influencing fraud detection: Step, Amount, Type.
- High recall (~82%) indicates good detection of fraudulent transactions.
- ROC AUC of 0.859 shows good distinction between fraud and non-fraud.
- Precision is low, indicating many false positives; requires further tuning.
- Balanced accuracy and MCC highlight the need for improved model balance.
- Introduced
isValidTransactionfeature improving detection accuracy. - Balanced dataset significantly improved recall.
- Naïve Bayes chosen for best recall and real-time applicability.
- Future enhancements: anomaly detection methods, adaptive thresholding, hybrid models, deep learning approaches, GAN for data augmentation.
- Integration of real-time transaction data and additional features like user behavior and geolocation.
- Research directions include Graph Neural Networks and blockchain technology.
- Deployment considerations: cloud deployment, big data handling, edge AI for mobile apps.
This project demonstrates a robust AI-powered financial fraud detection system designed to reduce losses, minimize false positives, and provide actionable insights for financial institutions.