Authors :
Jayanth Kande
Volume/Issue :
RISEM–2025
Google Scholar :
https://tinyurl.com/5yh2939d
Scribd :
https://tinyurl.com/499djwyt
DOI :
https://doi.org/10.38124/ijisrt/25jun178
Abstract :
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.
Keywords :
Anomaly Detection, Machine Learning, Financial Applications, Fraud Prevention, 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.
Keywords :
Anomaly Detection, Machine Learning, Financial Applications, Fraud Prevention, Risk Management.
