AI-Powered Fraud Detection in Financial Transactions

The growing sophistication of financial fraud poses significant challenges to traditional detection systems, which often fail to adapt to evolving patterns of fraudulent activity. This research explores the use of artificial intelligence (AI) to enhance fraud detection in financial transactions. By leveraging advanced machine learning models, including supervised, unsupervised, and deep learning techniques, the proposed framework offers a scalable and adaptive approach to identifying anomalies in real-time.
Key components of the framework include dynamic feature engineering, ensemble modeling, and the integration of explainable AI (XAI) to ensure transparency and regulatory compliance. The study evaluates the performance of various algorithms, such as Random Forests, Gradient Boosting Machines, and Autoencoders, on publicly available and proprietary transaction datasets. Results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced efficiency compared to traditional rule-based systems.
This research highlights the transformative potential of AI in fraud prevention, providing actionable insights for financial institutions to strengthen operational resilience and customer trust. By addressing challenges such as data imbalance and adversarial fraud techniques, the study offers a robust, real-time fraud detection system that meets the demands of modern financial ecosystems