Financial Fraud Detection Based on an Explainable Multi-Layer Framework

Financial information plays a critical role in decision-making for stakeholders, including investors, regulators, and corporate managers. However, financial data is susceptible to deliberate manipulation, where some firms may distort disclosures to mislead stakeholders and potentially engage in fraudulent activities. With the rapid expansion of capital markets and advancements in information technology, financial fraud has grown increasingly sophisticated and concealed. As a result, conventional detection methods often struggle to identify emerging fraud patterns, rendering fraud prevention increasingly complex and less effective. In this paper, we propose a novel multi-layer architecture model that integrates business, internal control, and strategic features. Our framework leverages multi-layer neural networks for effective feature extraction and concatenates their outputs for classification. Furthermore, we develop this framework by incorporating explainable artificial intelligence (XAI) techniques to enhance interpretability. Empirical results show that the proposed framework provides competitive discriminatory ability and produces conservative, low-false-alarm fraud warnings under the full multi-layer feature setting while also offering interpretable insights for the diverse needs of stakeholders. This study contributes to the development of fraud detection tools that are both operationally useful and interpretable.