{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:44:06Z","timestamp":1776876246983,"version":"3.51.2"},"reference-count":106,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017630","name":"Ministry of Education of the People's Republic of China Humanities and Social Sciences Youth Foundation","doi-asserted-by":"publisher","award":["24YJCZH032"],"award-info":[{"award-number":["24YJCZH032"]}],"id":[{"id":"10.13039\/501100017630","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72571184"],"award-info":[{"award-number":["72571184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72101166"],"award-info":[{"award-number":["72101166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71932008"],"award-info":[{"award-number":["71932008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.asoc.2026.115192","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:27:49Z","timestamp":1775575669000},"page":"115192","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Adversarial fraud sample generation with reinforcement learning: A joint optimization framework for financial statement fraud detection"],"prefix":"10.1016","volume":"197","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3769-4443","authenticated-orcid":false,"given":"Zhensong","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3238-288X","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4425-1955","authenticated-orcid":false,"given":"Aihua","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yinhong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Shi","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115192_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107487","article-title":"A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes","volume":"108","author":"Cheng","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115192_bib0010","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S1045-2354(03)00072-8","article-title":"Causes, consequences, and deterence of financial statement fraud","volume":"16","author":"Rezaee","year":"2005","journal-title":"Crit. Perspect. Account."},{"key":"10.1016\/j.asoc.2026.115192_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1911-3846.1996.tb00489.x","article-title":"Causes and consequences of earnings manipulation: an analysis of firms subject to enforcement actions by the SEC","volume":"13","author":"Dechow","year":"1996","journal-title":"Contemp. Account. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0020","series-title":"2015 International Conference on Humanities and Social Science Research","first-page":"156","article-title":"Study on the endogenous and preventive strategies of financial fraud in china\u2019s listed corporation under the new normal","author":"Ji","year":"2015"},{"key":"10.1016\/j.asoc.2026.115192_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2020.113421","article-title":"Deep learning for detecting financial statement fraud","volume":"139","author":"Craja","year":"2020","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111368","article-title":"Imbalanced credit card fraud detection data: a solution based on hybrid neural network and clustering-based undersampling technique","volume":"154","author":"Huang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115192_bib0035","first-page":"10","article-title":"Financial statement manipulation: ethical and regulatory perspectives","volume":"22","author":"Adejumo","year":"2025","journal-title":"GSC Adv. Res. Rev."},{"key":"10.1016\/j.asoc.2026.115192_bib0040","doi-asserted-by":"crossref","first-page":"67","DOI":"10.32996\/jefas.2024.6.1.7","article-title":"A review on financial fraud detection using AI and machine learning","volume":"6","author":"Kamuangu","year":"2024","journal-title":"J. Econ. Finance Account. Stud."},{"key":"10.1016\/j.asoc.2026.115192_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119559","article-title":"Detecting corporate financial fraud via two-stage mapping in joint temporal and financial feature domain","volume":"217","author":"Chen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.frl.2023.104458","article-title":"Enhancing financial fraud detection with hierarchical graph attention networks: a study on integrating local and extensive structural information","volume":"58","author":"Shi","year":"2023","journal-title":"Finance Res. Lett."},{"key":"10.1016\/j.asoc.2026.115192_bib0055","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1111\/ecpo.12283","article-title":"An intelligent detecting model for financial frauds in Chinese a-share market","volume":"36","author":"Sun","year":"2024","journal-title":"Econ. Polit."},{"key":"10.1016\/j.asoc.2026.115192_bib0060","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/j.1911-3846.2010.01041.x","article-title":"Predicting material accounting misstatements","volume":"28","author":"Dechow","year":"2011","journal-title":"Contemp. Account. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.econmod.2023.106428","article-title":"Textual analysis and detection of financial fraud: evidence from Chinese manufacturing firms","volume":"126","author":"Li","year":"2023","journal-title":"Econ. Model."},{"key":"10.1016\/j.asoc.2026.115192_bib0070","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3390\/jtaer20010026","article-title":"Expanding and interpreting financial statement fraud detection using supply chain knowledge graphs","volume":"20","author":"Zhu","year":"2025","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.accinf.2024.100693","article-title":"Using data-driven methods to detect financial statement fraud in the real scenario","volume":"54","author":"Zhou","year":"2024","journal-title":"Int. J. Account. Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0080","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","article-title":"Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","volume":"479","author":"Fiore","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2024.114381","article-title":"Can earnings conference calls tell more lies? A contrastive multimodal dialogue network for advanced financial statement fraud detection","volume":"189","author":"Lu","year":"2025","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.accinf.2025.100734","article-title":"Challenges and opportunities for artificial intelligence in auditing: evidence from the field","volume":"56","author":"Kokina","year":"2025","journal-title":"Int. J. Account. Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.frl.2023.104309","article-title":"A user-centered explainable artificial intelligence approach for financial fraud detection","volume":"58","author":"Zhou","year":"2023","journal-title":"Finance Res. Lett."},{"key":"10.1016\/j.asoc.2026.115192_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.frl.2023.104305","article-title":"Credit default prediction of Chinese real estate listed companies based on explainable machine learning","volume":"58","author":"Ma","year":"2023","journal-title":"Finance Res. Lett."},{"key":"10.1016\/j.asoc.2026.115192_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.frl.2023.104843","article-title":"Advancing financial fraud detection: self-attention generative adversarial networks for precise and effective identification","volume":"60","author":"Zhao","year":"2024","journal-title":"Finance Res. Lett."},{"key":"10.1016\/j.asoc.2026.115192_bib0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125211","article-title":"Fund transfer fraud detection: analyzing irregular transactions and customer relationships with self-attention and graph neural networks","volume":"259","author":"Shih","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0115","series-title":"Asian Conference on Machine Learning","first-page":"97","article-title":"CTAB-GAN: effective table data synthesizing","author":"Zhao","year":"2021"},{"key":"10.1016\/j.asoc.2026.115192_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114582","article-title":"Conditional wasserstein GAN-based oversampling of tabular data for imbalanced learning","volume":"174","author":"Engelmann","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0125","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1080\/01605682.2021.1880296","article-title":"Generative adversarial networks for data augmentation and transfer in credit card fraud detection","volume":"73","author":"Langevin","year":"2022","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.asoc.2026.115192_bib0130","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.ins.2022.12.056","article-title":"CHSMOTE: convex hull-based synthetic minority oversampling technique for alleviating the class imbalance problem","volume":"623","author":"Yuan","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0135","first-page":"5769","article-title":"Improved training of wasserstein GANS","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0140","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/00014788.2019.1614267","article-title":"21st century scandals: towards a risk approach to financial reporting scandals","volume":"49","author":"Camfferman","year":"2019","journal-title":"Account. Bus. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0145","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cose.2015.09.005","article-title":"Intelligent financial fraud detection: a comprehensive review","volume":"57","author":"West","year":"2016","journal-title":"Comput. Secur."},{"key":"10.1016\/j.asoc.2026.115192_bib0150","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1111\/j.1540-6261.1968.tb00843.x","article-title":"Financial ratios, discriminant analysis and the prediction of corporate bankruptcy","volume":"23","author":"Altman","year":"1968","journal-title":"J. Finance"},{"key":"10.1016\/j.asoc.2026.115192_bib0155","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2307\/2490395","article-title":"Financial ratios and the probabilistic prediction of bankruptcy","volume":"18","author":"Ohlson","year":"1980","journal-title":"J. Account. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0160","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2469\/faj.v55.n5.2296","article-title":"The detection of earnings manipulation","volume":"55","author":"Beneish","year":"1999","journal-title":"Financ. Anal. J."},{"key":"10.1016\/j.asoc.2026.115192_bib0165","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.eswa.2006.02.016","article-title":"Data mining techniques for the detection of fraudulent financial statements","volume":"32","author":"Kirkos","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0170","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.accinf.2017.06.004","article-title":"Enhancement of fraud detection for narratives in annual reports","volume":"26","author":"Chen","year":"2017","journal-title":"Int. J. Account. Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0175","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s11142-020-09563-8","article-title":"Using machine learning to detect misstatements","volume":"26","author":"Bertomeu","year":"2021","journal-title":"Rev. Account. Stud."},{"key":"10.1016\/j.asoc.2026.115192_bib0180","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1002\/for.70056","article-title":"Forecasting corporate bankruptcy throughclass-rebalanced self-training semi-constrainedmatrix factorization","volume":"45","author":"Chen","year":"2025","journal-title":"J. Forecast."},{"key":"10.1016\/j.asoc.2026.115192_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105895","article-title":"Value-added tax fraud detection with scalable anomaly detection techniques","volume":"86","author":"Vanhoeyveld","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115192_bib0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial fraud: a review of anomaly detection techniques and recent advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.techfore.2023.122527","article-title":"State of the art in financial statement fraud detection: a systematic review","volume":"192","author":"Shahana","year":"2023","journal-title":"Technol. Forecast. Soc. Change"},{"key":"10.1016\/j.asoc.2026.115192_bib0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2023.109118","article-title":"Tracking down financial statement fraud by analyzing the supplier-customer relationship network","volume":"178","author":"Li","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0205","first-page":"1","article-title":"Reading between the lines: detecting corporate financial fraud using multi-dimensional textual features","author":"Yao","year":"2025","journal-title":"Inf. Syst. Front."},{"key":"10.1016\/j.asoc.2026.115192_bib0210","first-page":"1","article-title":"Credit risk prediction for SMEs based on multi-view learning with hierarchical attention mechanism","author":"Chen","year":"2025","journal-title":"Ann. Oper. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0215","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.ejor.2022.10.032","article-title":"A transformer-based model for default prediction in mid-cap corporate markets","volume":"308","author":"Korangi","year":"2023","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2022.113913","article-title":"Attentive statement fraud detection: distinguishing multimodal financial data with fine-grained attention","volume":"167","author":"Wang","year":"2023","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0225","series-title":"2024 IEEE International Conference on Metaverse Computing, Networking, and Applications","first-page":"343","article-title":"Credit card fraud detection using advanced transformer model","author":"Yu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115192_bib0230","doi-asserted-by":"crossref","first-page":"62899","DOI":"10.1109\/ACCESS.2024.3387841","article-title":"A distributed knowledge distillation framework for financial fraud detection based on transformer","volume":"12","author":"Tang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115192_bib0235","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11704-024-40474-y","article-title":"Graph neural networks for financial fraud detection: a review","volume":"19","author":"Cheng","year":"2025","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0240","doi-asserted-by":"crossref","first-page":"920","DOI":"10.26599\/BDMA.2024.9020013","article-title":"Multi-relational graph representation learning for financial statement fraud detection","volume":"7","author":"Wang","year":"2024","journal-title":"Big Data Min. Anal."},{"key":"10.1016\/j.asoc.2026.115192_bib0245","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2712","article-title":"Company-as-tribe: company financial risk assessment on tribe-style graph with hierarchical graph neural networks","author":"Bi","year":"2022"},{"key":"10.1016\/j.asoc.2026.115192_bib0250","doi-asserted-by":"crossref","first-page":"5313","DOI":"10.1007\/s40747-023-01016-4","article-title":"Rdqn: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection","volume":"9","author":"Tekkali","year":"2023","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0255","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1998","article-title":"A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme","volume":"10","author":"Qayoom","year":"2024","journal-title":"Peerj Comput. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0260","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/OJCS.2025.3543450","article-title":"FraudGNN-RL: a graph neural network with reinforcement learning for adaptive financial fraud detection","volume":"6","author":"Cui","year":"2025","journal-title":"IEEE Open J. Comput. Soc."},{"key":"10.1016\/j.asoc.2026.115192_bib0265","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/OJCS.2025.3587001","article-title":"Driftshield: autonomous fraud detection via actor-critic reinforcement learning with dynamic feature reweighting","volume":"6","author":"Cao","year":"2025","journal-title":"IEEE Open J. Comput. Soc."},{"key":"10.1016\/j.asoc.2026.115192_bib0270","doi-asserted-by":"crossref","DOI":"10.3390\/app112110004","article-title":"Machine learning based on resampling approaches and deep reinforcement learning for credit card fraud detection systems","volume":"11","author":"Dang","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0275","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0280","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0285","series-title":"International Conference on Intelligent Computing","first-page":"878","article-title":"Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning","author":"Han","year":"2005"},{"key":"10.1016\/j.asoc.2026.115192_bib0290","series-title":"IEEE International Joint Conference on Neural Networks","first-page":"1322","article-title":"ADASYN: adaptive synthetic sampling approach for imbalanced learning","author":"He","year":"2008"},{"key":"10.1016\/j.asoc.2026.115192_bib0295","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/TKDE.2025.3544284","article-title":"A novel expandable borderline smote oversampling method for class imbalance problem","volume":"37","author":"Sun","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0300","first-page":"769","article-title":"Two modifications of CNN","volume":"6","author":"Tomek","year":"1976","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.asoc.2026.115192_bib0305","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume":"3","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.asoc.2026.115192_bib0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.123126","article-title":"Explainable fraud detection of financial statement data driven by two-layer knowledge graph","volume":"246","author":"Cai","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0315","first-page":"1","article-title":"C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling","volume":"11","author":"Drummond","year":"2003","journal-title":"Workshop Learn. Imbalanced Datasets II"},{"key":"10.1016\/j.asoc.2026.115192_bib0320","doi-asserted-by":"crossref","first-page":"10795","DOI":"10.1109\/TPAMI.2023.3268118","article-title":"Deep long-tailed learning: a survey","volume":"45","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115192_bib0325","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.procs.2022.11.185","article-title":"Using GNN to detect financial fraud based on the related party transactions network","volume":"214","author":"Mao","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2024.114231","article-title":"The information content of financial statement fraud risk: an ensemble learning approach","volume":"182","author":"Duan","year":"2024","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113190","article-title":"An attention-based balanced variational autoencoder method for credit card fraud detection","volume":"177","author":"Shi","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115192_bib0340","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.asoc.2026.115192_bib0345","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0350","first-page":"55","article-title":"Utility of synthetic data in finances: an application of online p2p lending loan default analysis","volume":"23","author":"Song","year":"2024","journal-title":"J. Inf. Technol. Serv."},{"key":"10.1016\/j.asoc.2026.115192_bib0355","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501305","article-title":"Generation of realistic synthetic financial time-series","volume":"18","author":"Dogariu","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0360","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"1286","article-title":"One-class adversarial nets for fraud detection","volume":"vol. 33","author":"Zheng","year":"2019"},{"key":"10.1016\/j.asoc.2026.115192_bib0365","doi-asserted-by":"crossref","first-page":"304","DOI":"10.3390\/make5010019","article-title":"A survey on GAN techniques for data augmentation to address the imbalanced data issues in credit card fraud detection","volume":"5","author":"Strelcenia","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.asoc.2026.115192_bib0370","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-022-00648-6","article-title":"The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey","volume":"9","author":"Sauber-Cole","year":"2022","journal-title":"J. Big Data"},{"key":"10.1016\/j.asoc.2026.115192_bib0375","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3390\/risks9030049","article-title":"Alleviating class imbalance in actuarial applications using generative adversarial networks","volume":"9","author":"Ngwenduna","year":"2021","journal-title":"Risks"},{"key":"10.1016\/j.asoc.2026.115192_bib0380","first-page":"42","article-title":"An overview of classification algorithms for imbalanced datasets","volume":"2","author":"Ganganwar","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0385","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","article-title":"Effective data generation for imbalanced learning using conditional generative adversarial networks","volume":"91","author":"Douzas","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115192_bib0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113540","article-title":"Gan-based approach for data imputation and handling class imbalance using one class ensemble","author":"Baro","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115192_bib0395","first-page":"7335","article-title":"Modeling tabular data using conditional GAN","volume":"32","author":"Xu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0400","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.14778\/3231751.3231757","article-title":"Data synthesis based on generative adversarial networks","volume":"11","author":"Park","year":"2024","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.asoc.2026.115192_bib0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120311","article-title":"AWGAN: an adaptive weighting GAN approach for oversampling imbalanced datasets","volume":"663","author":"Guan","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0410","author":"Li"},{"key":"10.1016\/j.asoc.2026.115192_bib0415","series-title":"International Conference on Machine Learning","first-page":"17564","article-title":"Tabddpm: modelling tabular data with diffusion models","author":"Kotelnikov","year":"2023"},{"key":"10.1016\/j.asoc.2026.115192_bib0420","series-title":"Proceedings of the Fourth ACM International Conference on AI in Finance","first-page":"64","article-title":"Findiff: diffusion models for financial tabular data generation","author":"Sattarov","year":"2023"},{"key":"10.1016\/j.asoc.2026.115192_bib0425","first-page":"1","article-title":"Diffusion models for tabular data imputation and synthetic data generation","volume":"19","author":"Villaiz\u00e1n-Vallelado","year":"2025","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.asoc.2026.115192_bib0430","doi-asserted-by":"crossref","first-page":"6450","DOI":"10.1109\/TKDE.2025.3608246","article-title":"Goio: generative oversampling approach to class imbalance and overlap of tabular data","volume":"37","author":"Ren","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0435","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110359","article-title":"Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions","volume":"197","author":"Xiong","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.asoc.2026.115192_bib0440","series-title":"2024 IEEE International Conference on Big Data","first-page":"6289","article-title":"Differentially private synthetic data generation using context-aware gans","author":"Kotal","year":"2024"},{"key":"10.1016\/j.asoc.2026.115192_bib0445","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1007\/s10489-025-06945-w","article-title":"Enhancing counterfactual explanations with causal inference: integrating directlingam and variational autoencoders for constraint-aware generation","volume":"55","author":"Dinh","year":"2025","journal-title":"Appl. Intell."},{"key":"10.1016\/j.asoc.2026.115192_bib0450","series-title":"IEEE International Conference on Data Mining","first-page":"413","article-title":"Isolation forest","author":"Liu","year":"2008"},{"key":"10.1016\/j.asoc.2026.115192_bib0455","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-based anomaly detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.asoc.2026.115192_bib0460","author":"Schulman"},{"key":"10.1016\/j.asoc.2026.115192_bib0465","author":"Mirza"},{"key":"10.1016\/j.asoc.2026.115192_bib0470","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1111\/j.1540-6261.2010.01614.x","article-title":"Who blows the whistle on corporate fraud?","volume":"65","author":"Dyck","year":"2010","journal-title":"J. Finance"},{"key":"10.1016\/j.asoc.2026.115192_bib0475","doi-asserted-by":"crossref","DOI":"10.3389\/fdata.2023.1296508","article-title":"CTAB-GAN+: enhancing tabular data synthesis","volume":"6","author":"Zhao","year":"2024","journal-title":"Front. Big Data"},{"key":"10.1016\/j.asoc.2026.115192_bib0480","first-page":"10","article-title":"Balancing training data for automated annotation of keywords: a case study","volume":"2","author":"Batista","year":"2003","journal-title":"Second Braz. Workshop Bioinform."},{"key":"10.1016\/j.asoc.2026.115192_bib0485","series-title":"International Conference on Machine Learning","first-page":"4393","article-title":"Deep one-class classification","author":"Ruff","year":"2018"},{"key":"10.1016\/j.asoc.2026.115192_bib0490","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"82","article-title":"Asymmetric loss for multi-label classification","author":"Ridnik","year":"2021"},{"key":"10.1016\/j.asoc.2026.115192_bib0495","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"10.1016\/j.asoc.2026.115192_bib0500","doi-asserted-by":"crossref","first-page":"e444","DOI":"10.7717\/peerj-cs.444","article-title":"Neural network hyperparameter optimization for prediction of real estate prices in Helsinki","volume":"7","author":"Kalliola","year":"2021","journal-title":"Peerj Comput. Sci."},{"key":"10.1016\/j.asoc.2026.115192_bib0505","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3991\/ijoe.v18i15.34399","article-title":"Artificial neural network hyperparameters optimization: a survey","volume":"18","author":"Kadhim","year":"2022","journal-title":"Int. J. Online Biomed. Eng."},{"key":"10.1016\/j.asoc.2026.115192_bib0510","first-page":"138","article-title":"Hyperparameter tuning on random forest for diagnose Covid-19","volume":"6","author":"Baita","year":"2023","journal-title":"J. Inform. dan Komput."},{"key":"10.1016\/j.asoc.2026.115192_bib0515","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115192_bib0520","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2623","article-title":"Optuna: a next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"10.1016\/j.asoc.2026.115192_bib0525","first-page":"1","article-title":"Stable-baselines3: reliable reinforcement learning implementations","volume":"22","author":"Raffin","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2026.115192_bib0530","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462600640X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462600640X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:59:41Z","timestamp":1776873581000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849462600640X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":106,"alternative-id":["S156849462600640X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115192","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adversarial fraud sample generation with reinforcement learning: A joint optimization framework for financial statement fraud detection","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115192","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115192"}}