Enhancing Financial Security Based on Machine Learning Techniques for Anomaly Detection in Fraud Transactions

Financial organizations face growing threats to their security because of digital banking along with online financial transactions. The research demonstrates a method to boost financial security that implements machine learning anomaly detection algorithms on fraudulent payment systems. The research utilizes the Credit Card Fraud (CCF) dataset with substantial discrepancy between authentic and fraudulent records while executing comprehensive data preprocessing techniques that utilize outlier identification methods in addition to random under-sampling strategies. The important features are comprised of 31 attributes that include anonymized variables (V1–V28) and transaction parameters (time and amount) with their assigned class label. The data has been partitioned into training, which takes up 70%, and testing, which occupies 30%. The method known as Isolation Forest (iForest) turns out to be the most effective classifier when tested on anomalous transactions with 98.65% accuracy coupled with 98.20% precision along with 98.64% recall and 98.52% F1-score performance. Anomaly detection-based machine learning methods indicate their clear ability to detect fraudulent transactions through both precise and high-recall manner. The results prove that sophisticated machine learning systems function as effective security instruments to stop financial system fraud.