Advanced Risk-Aware Fraud Detection Using Value-at-Risk Techniques for Skewed Data
Keywords:
Value-at-Risk (VaR), machine learning (ML) , skewed datasets , imbalanced data, anomaly detection, oversamplingAbstract
Financial systems can significantly benefit from employing value-at-risk (VaR) to identify instances of financial misconduct, especially when dealing with non-linear data. Unusual datasets and rare, expensive fraud transactions challenge conventional fraud detection methods. Machine learning models and Value at Risk (VaR) analyses look for unusual financial transaction patterns to detect fraud. Undersampling, oversampling, and fake data can improve model performance by addressing class imbalances. Complex algorithms like XGBoost, Random Forest, and deep learning models help detect fraud. Feature engineering, which integrates financial metrics and transaction patterns, simplifies prediction. Machine learning can detect fraud in real time, reducing financial institution risks. Explainable AI (XAI) detects fraud and ensures legal compliance. While maintaining system efficacy, this hybrid approach significantly reduces financial fraud losses.
