MACHINE LEARNING AND FINANCIAL FRAUD: NOVEL DEVELOPMENTS IN INFORMATION ANALYSIS

Abstract

In the digital age, financial fraud has become a serious problem that threatens the stability of financial institutions and undermines stakeholder trust. Traditional rule-based systems for detection have proven limits in keeping up with developing fraudulent schemes as fraudsters regularly modify their strategies. The use of machine learning techniques has completely changed the landscape of financial fraud detection and prevention in response to this expanding danger. The essential function that machine learning plays in preventing financial fraud. It starts off by giving a general review of the many forms and effects of financial fraud, such as investment fraud, account takeover, payment card fraud, and insurance fraud. A more comprehensive and flexible strategy is required in light of the catastrophic financial losses suffered by firms and people. The articles vague explores the potential of machine learning algorithms to examine huge amounts of transactional data and spot trends and abnormalities that point to fraud. We investigate the performance of supervised learning models, including Random Forests and Gradient Boosting, in identifying known fraud patterns. Additionally, unsupervised learning techniques like anomaly detection and clustering provide intriguing ways to spot fresh and previously undiscovered fraudulent activity. It emphasizes the value of feature engineering and data pretreatment in enhancing machine learning models for fraud detection. Additionally, the use of model stacking and ensemble approaches to increase accuracy and decrease false positives is highlighted. The abstract highlights the benefit of quick fraud identification and prevention, minimizing possible losses and preserving client confidence, since machine learning models function in real-time. The article discusses ways to assure model interpretability, fairness, and robustness against adversarial assaults while addressing the difficulties of deploying machine learning for financial fraud detection. Additionally stressed are ethical issues with data privacy and compliance.