Anomaly Detection in Financial Applications Leveraging Machine Learning for Fraud Prevention and Risk Management

The financial sector faces an ever-evolving landscape of fraudulent activities and complex risk management challenges. As financial transactions become more digital and instantaneous, traditional rule-based systems are increasingly inadequate in identifying sophisticated fraud schemes and anomalies (Bolton & Hand, 2002) [1]. These legacy systems often rely on predefined patterns, which makes them rigid and slow to adapt to novel threats (Ngai et al., 2011) [2]. Machine learning (ML) offers a dynamic and scalable solution by employing data-driven models that can identify complex and subtle patterns suggestive of fraudulent behavior (West & Bhattacharya, 2016) [3]. ML-based anomaly detection models can scan vast amounts of transactional data in real time and learn from both new and historical trends (Bhattacharyya et al., 2011) [4]. This paper proposes a comprehensive framework that integrates state-of-the-art machine learning methods to detect anomalies in financial applications. The approach emphasizes intelligent feature extraction, model optimization, and the evaluation of diverse algorithms to enhance detection accuracy and reduce false positives. By adopting this framework, financial institutions can proactively identify fraud, mitigate risks, and maintain operational integrity. In order to help develop safer and more adaptable fraud prevention strategies, the study also looks into the stability and scalability of machine learning models in real-world financial contexts.