The Use of Machine Learning Techniques for Detecting Financial Statement Fraud (gjstudies, 2021)
This research introduces a novel hybrid methodology for detecting financialstatement fraud by integrating quantum-inspired optimization algorithms with federated learning architectures, creating a privacy-preserving, adaptive detection system that addresses limitations in conventional approaches. Traditional fraud detection models typically rely on static datasets and centralized processing, whichnot only compromise data privacy but also fail to adapt to evolving fraud patternsin real-time. Our approach uniquely combines three innovative components: aquantum annealing-inspired feature selection mechanism that identifies subtle, nonlinear relationships in financial data; a federated learning framework that enablescollaborative model training across financial institutions without sharing sensitivetransactional data; and an adaptive drift detection module that continuously monitors for concept drift in fraud patterns. We developed and tested our methodologyusing a synthetically generated dataset simulating real-world financial statementanomalies across multiple banking institutions, incorporating features derived fromforensic accounting principles. Results demonstrate a 23.7