Detecting Earnings Management through Explainable Artificial Intelligence: A Machine Learning Approach Using Financial Statement Data
DOI:
https://doi.org/10.70568/ijdafs.3.1.1Keywords:
Earnings Management Proxy, Discretionary Accruals, Explainable Artificial Intelligence, SHAP, Machine LearningAbstract
This paper develops an explainable machine learning framework to classify the firm-year observations with unusually high discretionary accruals, a common proxy for the accrual-based earnings management, by using the financial statement data. The sample includes 7,800 firm-year observations of publicly listed non-financial firms from Compustat North America (via WRDS) for firms listed on the NYSE, Nasdaq and NYSE American over the fiscal years 2010-2023. The discretionary accruals were estimated by the Modified Jones Model, and they were used to build a proxy-based binary label according to the top quintile of the absolute discretionary accruals inside each industry-year (1,560 observations, 20.0%, classified as high-accrual). Four algorithms, which are the Logistic Regression, Random Forest, XGBoost and LightGBM, were trained on a leakage-screened set of financial ratios that excludes every component used in the label, and they were compared with the Beneish M-Score. The models were trained on the 2010-2018 data, tuned on 2019-2020, and evaluated out-of-sample on 2021-2023 (1,000 observations). The XGBoost achieves the highest ROC-AUC (0.748) and PR-AUC (0.421), which is higher than the Beneish M-Score (0.612), while the LightGBM achieves the highest F1-score (0.484) and balanced accuracy (0.653) at the 0.50 threshold, and the ROC-AUC advantage of the ML models is statistically significant (DeLong test, p = 0.004). Moreover, the SHAP analysis of the optimal model shows that the days sales uncollected, the gross profit margin and the debt-to-equity ratio are the leading variables, and an illustrative walkthrough shows how the SHAP output can be turned into a line-item diagnostic report. The findings indicate that the explainable tree-based models can improve the classification of the high-accrual firm-years compared to the Beneish M-Score, while keeping the interpretability that the auditors and the regulators need.
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