Data Mining techniques for the detection of fraudulent financial statements

TL;DR: This study develops machine learning models to detect financial statement fraud, leveraging an extensive dataset and ensemble methods (Random Forest, XGBoost) to improve accuracy, recall, and AUC-ROC scores, highlighting the importance of non-financial indicators in fraud detection.

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.

TL;DR: The study identifies potential manipulations in the financial statements of Indian companies and finds that profitability ratio acts as a significant predictor of fraud.

TL;DR: In this paper, a set of financial and economic ratios are investigated in a bankruptcy prediction context wherein a multiple discriminant statistical methodology is employed, and the data used in the study are limited to manufacturing corporations, where an initial sample of sixty-six firms is utilized to establish a function which best discriminates between companies in two mutually exclusive groups: bankrupt and nonbankrupt firms.

TL;DR: In this paper, the authors empirically tested the prediction that the inclusion of larger proportions of outside members on the board of directors significantly reduces the likelihood of financial statement fraud and found that no-fraud firms have boards with significantly higher percentages of outside board members than fraud firms.

TL;DR: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making.