Hybrid Machine Learning for Fraud Detection: Balancing Accuracy and Security in Digital Transactions

Nowadays, frauds would occur by hackers and non-legitimate users, which would create losses for specific users and damage their identity.The kinds of fraud that popularly happen are transaction fraud, card fraud (card not present), phishing, and account takeover.To prevent and minimize the losses, the hybrid model is designed and demanded.The combination of random forests, LightGBM, and Ensemble is used to improve overall performance and accuracy improvement and ensure privacy and security concerns.In this methodology, random forests reduce overfitting, support large datasets, prefer ranked features, are less sensitive to noise, and result in improvement in accuracy.The role of LightGBM is to ensure boosting in speed and memory usage, support large datasets and imbalanced datasets, and ensure reduced false positives and false negatives.The necessity of an ensemble strategy in this scenario is to combine the benefits of random forest and LightGBM, ensure overall performance, and eliminate flagging legitimate transactions as fraudulent.The performance measures are evaluated and compared against the considered models in this domain.