Financial Fraud Identification and Interpretability Study for Listed Companies Based on Convolutional Neural Network

TL;DR

This study develops a CNN-based framework for early detection and interpretability of financial fraud in listed Chinese companies by transforming financial data into image-like formats, outperforming traditional models.

Contribution

It introduces a novel feature engineering scheme transforming panel data into images for CNN analysis and provides interpretability insights into fraud predictors and patterns.

Findings

CNN outperforms logistic regression and LightGBM in accuracy and early warning.

Key fraud predictors include solvency, governance, and environmental factors.

Fraud firms show heterogeneous short-term feature patterns.

Abstract

Since the emergence of joint-stock companies, financial fraud by listed firms has repeatedly undermined capital markets. Fraud is difficult to detect because of covert tactics and the high labor and time costs of audits. Traditional statistical models are interpretable but struggle with nonlinear feature interactions, while machine learning models are powerful but often opaque. In addition, most existing methods judge fraud only for the current year based on current year data, limiting timeliness. This paper proposes a financial fraud detection framework for Chinese A-share listed companies based on convolutional neural networks (CNNs). We design a feature engineering scheme that transforms firm-year panel data into image like representations, enabling the CNN to capture cross-sectional and temporal patterns and to predict fraud in advance. Experiments show that the CNN outperforms logistic regression and LightGBM in accuracy, robustness, and early-warning performance, and that proper tuning of the classification threshold is crucial in high-risk settings. To address interpretability, we analyze the model along the dimensions of entity, feature, and time using local explanation techniques. We find that solvency, ratio structure, governance structure, and internal control are general predictors of fraud, while environmental indicators matter mainly in high-pollution industries. Non-fraud firms share stable feature patterns, whereas fraud firms exhibit heterogeneous patterns concentrated in short time windows. A case study of Guanong Shares in 2022 shows that cash flow analysis, social responsibility, governance structure, and per-share indicators are the main drivers of the model's fraud prediction, consistent with the company's documented misconduct.