An Explainable Temporal Machine Learning Framework for Financial Fraud Risk Prediction in Listed Institutions
DOI:
https://doi.org/10.54097/hkcxmt18Keywords:
Financial statement fraud, explainable artificial intelligence, rare-event classification, temporal features, selective prediction, company-isolated validation.Abstract
Financial-statement fraud screening is a rare-event, high-stakes task in which random row splits, enforcement-derived leakage, and unstable post-hoc explanations can produce misleadingly optimistic evidence. This paper proposes ESGE, an Explainable Stability-Gated Ensemble that combines leakage auditing, strictly prior-only temporal features, validation precision-recall-area-weighted ensembling of logistic regression, LightGBM, and CatBoost, an inter-model disagreement gate, and cross-fold-stable TreeSHAP reporting. Experiments use the public company-isolated Financial Statement Fraud Detection benchmark: 10,159 U.S. listed-firm quarters, 511 fraud observations (5.03%), and 5,658 companies. No company appears in both training and test sets in any fold. ESGE obtains a five-fold ROC-AUC of 0.9032±0.0198 and PR-AUC of 0.6344±0.0648; the top 10% of ranked filings contain 72.22%±5.07% of fraud observations. Relative to the strongest base learner, pooled PR-AUC improves by 0.0186 (95% paired stratified-bootstrap interval 0.0074–0.0294, p=0.002). At 79.22% mean accepted coverage, the disagreement gate raises conditional PR-AUC to 0.7212±0.0608. A finance/insurance/real-estate subgroup analysis yields ROC-AUC 0.8569 and PR-AUC 0.5008. Results, code, predictions, leakage logs, and explanation values are supplied for exact reproduction.
Downloads
References
[1] Waffo Dzuyo, G. S., Guibon, G., Cerisara, C., & Belmar Letelier, L. (2026). Benchmarking generalization in financial statement fraud detection: Robust evaluation and novel tasks. arXiv preprint arXiv:2607.19259.
[2] U.S. Securities and Exchange Commission. (n.d.). Financial statement data sets. SEC Data Library. Retrieved July 31, 2026.
[3] USC Leventhal School of Accounting. (n.d.). AAER dataset. Retrieved July 31, 2026.
[4] Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540 6261.1968.tb00843.x
[5] Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36. https://doi.org/10.2469/faj.v55.n5.2296
[6] Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. https://doi.org/10.2307/2491047
[7] Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911 3846.2010.01041.x
[8] Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995–1003. https://doi.org/10.1016/j.eswa.2006.02.016
[9] Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt 50009
[10] Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50(2), 491–500. https://doi.org/10.1016/j.dss.2010.11.006
[11] Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475 679X.12292
[12] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
[13] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/j.cose.2015.09.005
[14] Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113. https://doi.org/10.1016/j.jnca.2016.04.007
[15] Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
[16] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
[17] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (pp. 3146–3154).
[18] Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems 31 (pp. 6638–6648).
[19] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765–4774).
[20] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778
[21] Arrieta, A. B., Díaz Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
[22] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93. https://doi.org/10.1145/3236009
[23] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256 019 0048 x
[24] Saito, T., & Rehmsmeier, M. (2015). The precision recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
[25] Davis, J., & Goadrich, M. (2006). The relationship between precision recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (pp. 233–240). https://doi.org/10.1145/1143844.1143874
[26] Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
[27] Niculescu Mizil, A., & Caruana, R. (2005). Predicting good probabilities with supervised learning. In Proceedings of the 22nd International Conference on Machine Learning (pp. 625–632). https://doi.org/10.1145/1102351.1102430
[28] Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1–3. https://doi.org/10.1175/1520 0493(1950)078<0001:VOFEIT>2.0.CO;2
[29] He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
[30] Hand, D. J. (2009). Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning, 77, 103–123. https://doi.org/10.1007/s10994 009 5119 5
[31] Ping, W., Jiao, Y., Fan, H., & Zhang, X. (2026). Multimodal fraud detection in financial statements: A trimodal attention network with contrastive evidence chain construction. IEEE Access, 14, 80456–80468. https://doi.org/10.1109/ACCESS.2026.3695466
[32] Chen, Z., Wang, M., Zeng, Z., & Ping, W. (2026). Uncertainty aware financial forecasting: Leveraging conformal prediction for risk adjusted models. IEEE Access, 14, 90053–90077. https://doi.org/10.1109/ACCESS.2026.3702666
[33] Chen, J., Liu, J., Liang, Y., & Zhou, M. (2026). KE MLLM: A knowledge enhanced multi sensor learning framework for explainable fake review detection. Applied Sciences, 16(6), 2909. https://doi.org/10.3390/app16062909
[34] Fan, H., Jiao, Y., Wang, M., Chen, J., & Yang, S. (2026). Fraud learns too: Continual graph learning under strategic adversarial drift in dynamic networks. Scientific Reports. https://doi.org/10.1038/s41598 026 60997 7
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Highlights in Business, Economics and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
