Intelligent Systems in Accounting, Finance and Management最新文献

A Stacking-Based Hybrid Machine Learning Framework for Detecting Cost-of-Sales Manipulation in Data-Constrained Financial Statements 基于堆叠的混合机器学习框架，用于检测数据约束财务报表中的销售成本操纵

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-09-27 DOI: 10.1002/isaf.70047

Aysel Topsir, Melih Agraz, Selcuk Cebi

{"title":"A Stacking-Based Hybrid Machine Learning Framework for Detecting Cost-of-Sales Manipulation in Data-Constrained Financial Statements","authors":"Aysel Topsir, Melih Agraz, Selcuk Cebi","doi":"10.1002/isaf.70047","DOIUrl":"https://doi.org/10.1002/isaf.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>Detecting financial statement fraud in emerging markets is difficult due to scarce labeled data and severe class imbalance. This study develops a stacking-based hybrid machine learning framework for detecting cost-of-sales manipulation in firms listed in Türkiye. Rather than proposing a general fraud/non-fraud detection model, the study focuses on fraud-type-specific detection for cost-of-sales manipulation under data-constrained conditions. Because publicly labeled fraud cases are limited, a Türkiye-specific semi-synthetic dataset is constructed from KAP financial disclosures, in which unmodified financial statements are combined with manipulated versions generated through rule-based, audit-driven manipulation scenarios and domain expert knowledge. The proposed hybrid architecture combines diverse base learners through stacking to capture both linear and nonlinear structures in financial ratios, with the aim of improving generalization and reducing misclassification. Empirical results show that the stacking hybrid model yields higher point estimates than the strongest baseline model across most evaluation metrics and markedly reduces false-positive classifications. Paired bootstrap confidence intervals support the improvement in fraud-class precision, whereas the differences in the remaining metrics fall within sampling uncertainty. The resulting error distribution is therefore more favorable for audit planning and decision support.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149068572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Evaluation Framework for Post-Trained Bookkeeping Language Models 培训后记账语言模型的评估框架

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-08-20 DOI: 10.1002/isaf.70046

Mario Zupan

引用次数: 0

Experimenting With DLT: A Comparison Between the EU DLT Pilot Regime Regulation and the UK Digital Securities Sandbox DLT试验：欧盟DLT试点制度监管与英国数字证券沙盒的比较

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-08-20 DOI: 10.1002/isaf.70045

Randy Priem

引用次数: 0

Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach 企业破产预测的先进机器学习技术：梯度增强树网方法

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-07-23 DOI: 10.1002/isaf.70044

Ali Saeedi

引用次数: 0

Artificial Intelligence and Corporate Social Responsibility: A Systematic Review of Emerging Integration, Mechanisms, and Challenges 人工智能与企业社会责任：新兴整合、机制和挑战的系统回顾

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-07-19 DOI: 10.1002/isaf.70043

Woon Leong Lin, Aranka Ignasiak-Szulc

{"title":"Artificial Intelligence and Corporate Social Responsibility: A Systematic Review of Emerging Integration, Mechanisms, and Challenges","authors":"Woon Leong Lin, Aranka Ignasiak-Szulc","doi":"10.1002/isaf.70043","DOIUrl":"https://doi.org/10.1002/isaf.70043","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines the emerging integration of artificial intelligence (AI) into corporate social responsibility (CSR) through a systematic literature review covering 2013–2024. Drawing on PRISMA-guided screening, thematic coding, and bibliometric mapping, the review identifies five recurrent empirical clusters: sustainability and resource optimization, ethical governance, transparency and accountability, stakeholder engagement, and CSR innovation. Rather than treating these clusters as the primary contribution, the study develops a mechanism-based explanation of AI–CSR relationships. Specifically, the review highlights three recurring generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighting. These mechanisms help explain how AI can improve CSR performance, legitimacy, and responsiveness under specific boundary conditions, while also generating risks such as assurance gaps, accountability diffusion, stakeholder exclusion, and metric crowding-out. The bibliometric evidence suggests increasing topical proximity and methodological coupling between AI and CSR but only limited evidence of full theoretical integration. Accordingly, the paper reframes the field as an emerging area of integration rather than a completed convergence. The study concludes by offering a more cautious theoretical synthesis, a set of aligned propositions, and an agenda for future research on the responsible use of AI in CSR governance and practice.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Class-Imbalance-Aware Adaptive Dataset Distillation for Scalable Pretrained Model in Credit Scoring 信用评分中可扩展预训练模型的类不平衡感知自适应数据蒸馏

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-07-05 DOI: 10.1002/isaf.70042

Xia Li, Hanghang Zheng, Xiwei Zhuang, Zhong Wang, Xiao Chen, Hong Liu, Jasmine Bai, Mao Mao

{"title":"Class-Imbalance-Aware Adaptive Dataset Distillation for Scalable Pretrained Model in Credit Scoring","authors":"Xia Li, Hanghang Zheng, Xiwei Zhuang, Zhong Wang, Xiao Chen, Hong Liu, Jasmine Bai, Mao Mao","doi":"10.1002/isaf.70042","DOIUrl":"https://doi.org/10.1002/isaf.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of artificial intelligence has significantly enhanced credit-scoring technologies. Despite the remarkable efficacy of advanced deep learning models, mainstream adoption continues to favor tree-structured models due to their robust predictive performance on tabular data. Although pretrained models have seen considerable development, the use of such models for tabular-structured credit-scoring datasets remains largely unexplored. Tabular-oriented large models, such as TabPFN, have made the application of large models in credit-scoring feasible, albeit they can only process limited sample sizes. This paper proposes a novel framework that combines a tabular-tailored dataset distillation technique with a pretrained model, thereby improving the scalability of TabPFN. Furthermore, although class imbalance is a common characteristic of financial datasets, its influence during dataset distillation has not been systematically explored; we therefore integrate imbalance-aware objectives into the distillation process, resulting in improved performance on imbalanced credit data. Experiments on six publicly available credit-scoring datasets show that the proposed imbalance-aware distillation improves AUC by up to 8.7 percentage points over the standard MSE-based distillation baseline. At the same time, distilled datasets retain roughly 76%–95% of the full-data AUC while often using under 10% of the original training samples (and at most 31.3%) and consistently outperforming random subsets. Taken together, these results demonstrate that imbalance-aware dataset distillation provides a compact yet accurate solution for imbalanced credit scoring and enhances the practical scalability of tabular pretrained models such as TabPFN.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Some Large Language Model Applications at the Big Four: Demonstration-Based Field Work 一些大型语言模型在四大的应用：基于演示的实地工作

IF 2

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-07-01 DOI: 10.1002/isaf.70041

Daniel E. O'Leary

引用次数: 0

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-05-14

引用次数: 0

Severity-Based Prioritisation of Administrative Tax Non-Compliance: A Multiclass Prediction Approach Using Financial and Governance Data 基于严重程度的行政税收不合规优先排序：使用财务和治理数据的多类别预测方法

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-04-08 DOI: 10.1002/isaf.70036

Lenka Papíková, Mário Papík

{"title":"Severity-Based Prioritisation of Administrative Tax Non-Compliance: A Multiclass Prediction Approach Using Financial and Governance Data","authors":"Lenka Papíková, Mário Papík","doi":"10.1002/isaf.70036","DOIUrl":"https://doi.org/10.1002/isaf.70036","url":null,"abstract":"<p>Tax authorities face growing volumes of filings and payments and must manage procedural non-compliance (e.g., late filing, late payment and the accumulation of tax arrears) with limited administrative capacity. Many existing machine learning (ML) and artificial intelligence (AI) applications in tax administration rely on binary outcomes, which limits severity-based prioritisation and the targeting of low-cost interventions. This study develops a multiclass prediction model for administrative tax compliance severity using Slovakia's public tax reliability index, which classifies companies into three categories based on regulator-defined administrative criteria. Using only financial statement ratios and governance indicators, we evaluate nine classifiers and five resampling techniques for class imbalance. Gradient boosting models (XGBoost and CatBoost) perform best, reaching an OvR AUC-ROC above 96% for 1-year forecasts, with modest declines for 2- and 3-year horizons. SHAP explanations indicate that smaller boards and indicators consistent with liquidity constraints and tax-payment pressure are associated with higher-severity administrative classes. The proposed workflow offers a transferable framework for multiclass, long-horizon compliance risk prediction and can support proactive case management (e.g., targeted reminders and payment facilitation, including payment plans, and debt prioritisation) in advance of the regulator's semi-annual updates; it may also provide researchers with a potential early-warning label of administrative compliance frictions that could be examined in relation to financial distress.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diversity Snapshots: Intermodal Analysis of Firm Diversity Discourse 多样性快照：企业多样性话语的多模式分析

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-04-07 DOI: 10.1002/isaf.70035

Jacqueline Gagnon, Alisher Mansurov

{"title":"Diversity Snapshots: Intermodal Analysis of Firm Diversity Discourse","authors":"Jacqueline Gagnon, Alisher Mansurov","doi":"10.1002/isaf.70035","DOIUrl":"https://doi.org/10.1002/isaf.70035","url":null,"abstract":"<p>Pictures are a powerful medium to communicate complex and emotive messages. In particular, the human face expresses corporate culture including diversity and equal opportunity. However, despite the recent visual turn in accounting and finance, quantitative research on diversity in photos is scant because automated solutions for identifying and classifying human faces were not readily available. This paper seeks to bridge this gap by tailoring automated large-sample facial analysis from the recent computing literature into the accounting literature. Our automated model identifies and classifies faces with sufficient accuracy and precision to draw reliable inferences, and this model is made available for future research. We use the resulting quantitative dataset to analyse intermodal discourse in the annual report, asking the question: Do cover photos augment textual diversity disclosure, or are they PR window-dressing? Results suggest that the decision to publish faces on the annual report cover is associated with an integrated reporting strategy and high-quality diversity disclosure, consistent with pictures augmenting textual disclosures. Gender and ethnic diversity of faces in cover photos tell a different story, tending towards PR window-dressing. Methods and findings from this paper may be of interest to researchers, government and policy makers involved in diversity research and regulation.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0