Supervised Learning Models for Enhancing Financial Fraud Detection Systems

Abstract

Companies require complex machine learning methods to detect financial fraud effectively because prejudice detection demands accurate identification of illegal activities. The proposed model merges three components including LSTM networks with transaction-based features alongside autoencoders for advanced financial fraud detection in transaction processing. LSTM networks enable the approach to achieve both pattern analysis and detection of irregular patterns in transaction sequences. The abnormal transaction detection task of Autoencoders involves learning regular patterns in transactions while detecting exceptional cases as outliers. The model's interpretability is improved through the integration of features that are based on transactions including frequency data and transaction amount along with geolocation information. Imbalanced data becomes manageable through the implementation of a fraud detection system that unites supervised with unsupervised learning techniques for improving fraud classification accuracy as well as minimizing false positive rates. Results from operating on actual financial data prove that this approach finds more fraudulent conduct better than classical machine learning identification methods. The proposed design presents an adaptable scheme which provides reliable real-time financial security applications.