The Use of Machine Learning Techniques for Detecting Financial Statement Fraud

Abstract

This research introduces a novel hybrid methodology for detecting financial
statement 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, which
not only compromise data privacy but also fail to adapt to evolving fraud patterns
in real-time. Our approach uniquely combines three innovative components: a
quantum annealing-inspired feature selection mechanism that identifies subtle, nonlinear relationships in financial data; a federated learning framework that enables
collaborative model training across financial institutions without sharing sensitive
transactional data; and an adaptive drift detection module that continuously monitors for concept drift in fraud patterns. We developed and tested our methodology
using a synthetically generated dataset simulating real-world financial statement
anomalies across multiple banking institutions, incorporating features derived from
forensic accounting principles. Results demonstrate a 23.7