Study on Enhancing Fraud Detection in Banking Transactions Using Advanced Machine Learning Techniques

Abstract

Introduction: The banking sector is vital for financial constancy in addition to economic development, allowing capital movement and credit in addition to liquidity. However, through improved digitalization, banks face new risks such as scams and money valeting, in addition to cyberattacks. Traditional fraud detection systems are inadequate for detecting evolving threats, leading to an essential need for advanced machine learning models that can perceive anomalies in real time and also adapt to new fraud patterns. In cities like Mumbai, where digital banking is quickly growing, implementing ML-based fraud detection is vital towards safeguarding financial institutions besides consumer conviction.

Objectives: This research discovers improvements in machine learning (ML) methods aimed at anomaly detection in the banking sector, concentrating on the financial ecosystem. It purposes to review present ML-based anomaly detection methods, measure their strengths besides limitations in risk management, and estimate their applicability in addressing challenges corresponding to fraud detection besides data imbalance. The study similarly highlights future research directions besides hands-on considerations aimed at improving fraud detection accuracy in banking. Finally, it searches to deliver understandings for enhancing banking security and addressing current challenges.

Methods: This study practices a mixed-methods approach, merging qualitative perceptions from surveys and interviews with quantitative analysis of case studies. It discovers machine learning (ML) techniques for anomaly detection in banking, focusing on hybrid models that integrate supervised and unsupervised learning methods. Data collection comprises replies from banking professionals, and then the PaySim dataset aimed at fraud simulation. The research assesses the effectiveness of various ML models and the importance of the challenges and benefits of ML adoption in financial risk management.

Results: The survey exposed important insights hooked on ML-based anomaly detection in banking, accentuating the most common anomalies, such as identity theft (22.4%) and fraud (17.2%). Hybrid models (30%) combining supervised and unsupervised learning remained the most widely used, followed by deep learning techniques (28.5%). Random Forest (99.30%) in addition to Gradient Boosting (98.50%) remained the most accurate models. Challenges identified included high false positives and trouble detecting novel fraud patterns, besides issues with model transparency besides interpretability. The usage of hybrid models remains particularly effective in addressing diverse fraud types.

Conclusions: This study demonstrates how effective hybrid and deep learning models are in identifying intricate banking fraud trends in spite of obstacles like data imbalance. It emphasizes the necessity of both adapting to new fraud strategies and continuously enhancing the interpretability of the model. In order to improve detection accuracy and compliance, future research would concentrate on boosting algorithms such as XGBoost in addition to LightGBM.