Enhanced Financial Fraud Detection via SISAE-METADES: A Supervised Deep Representation and Dynamic Ensemble Approach

Abstract

Detecting financial reporting fraud is vital for preserving market integrity and protecting investors from substantial losses. Yet, the challenges of high dimensionality and noisy financial data often undermine the effectiveness of existing financial fraud detection systems. To address these issues, this study proposes SISAE-METADES, a novel framework that integrates a supervised input-enhanced stacked autoencoder (SISAE) with a meta-learning–based dynamic ensemble selection (METADES) strategy. Unlike conventional stacked autoencoders, SISAE concatenates the original input at each encoding stage and incorporates label supervision, thereby learning task-relevant and class-discriminative representations. These enriched deep features improve both the diversity and competence of base classifiers and enable METADES to achieve more reliable local competence estimation. We validate the proposed framework using financial statement data from Chinese A-share listed companies (2005–2023), covering 71 indicators. Experimental results show that SISAE-METADES significantly outperforms standalone SISAE, traditional METADES, and several state-of-the-art baselines. In particular, it achieves substantial improvements in accuracy, recall, and F1-score, underscoring the robustness and effectiveness of combining supervised deep representation learning with dynamic ensemble selection for financial fraud detection. These findings highlight the framework’s practical significance in reducing investor losses, strengthening market confidence, and promoting the stability of the financial system.