Intelligent Systems in Accounting, Finance and Management最新文献

{"title":"Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products","authors":"Haiying Ren,&nbsp;Jun Guan,&nbsp;Jingru Guo","doi":"10.1002/isaf.70025","DOIUrl":"https://doi.org/10.1002/isaf.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the limitations of existing product concept design (PCD) methods in the rapidly changing market environments, this study proposes a PCD method using e-commerce product data and artificial intelligence techniques. First, data of competing e-commerce products are acquired from an e-commerce platform. Second, monthly sales of products are categorized and selected as the indicator for evaluating product concepts (PCs). Third, Doc2Vec is used to vectorize the product description to obtain the semantic representation of PCs, and a machine learning-based PC evaluation model is built using the concept vector as features. Finally, a PC element library is built based on Word2Vec, and the tabu search algorithm is applied to identify the optimal combination of concept elements, determining the most favorable combination of PCs for the new product. Results indicate that the PC evaluation model based on multilayer perceptron achieves an average accuracy of 85.62% in predicting the quartiles of sales in the case of middle-aged and elderly home products, with the area under the receiver operating characteristic curve ranging from 0.96 to 0.99. The proposed PCD method can produce novel PCs with good market potential and a high degree of automation, improving the time efficiency and quality of PCD.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891321","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}

{"title":"Complexity and Heterogeneity in Cryptocurrency Prices: An Analysis Based on Gaussian Mixture Model and Consensus Clustering","authors":"Tâmara Leal,&nbsp;Pedro Campos,&nbsp;Carlos Alves","doi":"10.1002/isaf.70024","DOIUrl":"https://doi.org/10.1002/isaf.70024","url":null,"abstract":"<p>This study investigates the daily price patterns and behavioral similarities among cryptocurrencies, focusing on two key research questions: (1) Do cryptocurrency prices vary consistently throughout the day? (2) Can cryptocurrencies be meaningfully grouped based on their behavioral patterns? Using Gaussian mixture models (GMMs), we analyze the opening, closing, high, and low prices of a broad range of cryptocurrencies. The findings reveal that while opening prices exhibit uniform patterns, closing, high, and low prices show more complex, multi-component behaviors, reflecting diverse market dynamics throughout the day. Consensus clustering identifies four distinct cryptocurrency clusters, each demonstrating unique price behaviors, challenging the notion of cryptocurrencies as a homogeneous group. The results suggest that cryptocurrencies behave as differentiated financial products, influenced by factors such as volatility, adoption, and technology. These findings contribute to the understanding of cryptocurrency market dynamics and have implications for investment strategies, risk management, and regulatory approaches.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824810","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}

{"title":"Addressing the Exception Prioritization Problem in Continuous Auditing Systems With Thresholding","authors":"Jan Svanberg,&nbsp;Peter Öhman,&nbsp;Isak Samsten,&nbsp;Presha E. Neidermeyer,&nbsp;Tarek Rana,&nbsp;Mats Danielson","doi":"10.1002/isaf.70022","DOIUrl":"https://doi.org/10.1002/isaf.70022","url":null,"abstract":"<p>Continuous auditing research has grappled with the challenge of managing the abundance of detected exceptions in internal audit applications for the past 30 years. A key issue in continuous auditing involves the uncontrolled proliferation of exceptions, where the sheer volume makes manual follow-up impractical, undermining the viability of the technology. The root cause of this problem is the combination of strong class imbalance and the predominant rule-based systems design. Prior investigations have attempted ad hoc remedies like introducing additional layers to prioritize the most suspicious exceptions or aggregating data. Currently, there is no universal method to address this prioritization challenge, leaving internal auditors without a means to focus specifically on exceptions most likely to represent genuine faults. Our research explores the origin of this prioritization dilemma and proposes a systems design that can deal appropriately with class imbalance. This solution allows full control of the exception volume by a simple approach in machine learning called thresholding and combined with methods to interpret the output of a continuous auditing system our design effectively focuses the internal auditors' attention on the most significant exceptions. We discuss the implications of thresholding for practice and the literature.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750746","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}

{"title":"Pay for Performance: A Comparative Analysis of Machine Learning Models for CEO Compensation Prediction","authors":"Mahfuja Malik,&nbsp;Eman G. Abdelfattah","doi":"10.1002/isaf.70023","DOIUrl":"https://doi.org/10.1002/isaf.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>The objective of our study is to demonstrate the feasibility of predicting chief executive officer (CEO) compensation by exploring various machine learning methods. In our analysis, we examine six models: <i>k</i>-nearest neighbors, random forest, decision tree, extra trees, extreme gradient boosting, and support vector machines regressors. We find that XGBoost, random forest, and extra trees regressors exhibit the highest predictive power with the lowest error. Decision tree feature importance analyses identify firm size, CEO age, tangibility, cash holding, and Tobin's Q as key factors in predicting CEO compensation. We also conduct ordinary least squares regressions and find that the significance levels of the coefficients are comparable to the feature importances from the machine learning analysis. Our feature-grouping analysis shows that firms' financial performance and economic characteristics play the most significant role in determining CEO compensation, followed by board characteristics. The analysis further indicates that the predictive power of random forest and extra trees is stronger for forecasting next year's compensation than for predicting the current year's. These findings are valuable for compensation consultants and stakeholders involved in benchmarking decisions.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695077","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}

{"title":"Financial Statement Fraud Detection via Large Language Models","authors":"Zehra Erva Ergun,&nbsp;Emre Sefer","doi":"10.1002/isaf.70021","DOIUrl":"https://doi.org/10.1002/isaf.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>With the widespread adoption of Internet-based AI technologies, addressing financial fraud has become increasingly critical, particularly within the realm of machine learning. In this case, deep learning and natural language processing (NLP) techniques offer powerful means of detecting fraudulent activity by analyzing financial documents, thereby enhancing both the efficiency and precision of such assessments and supporting financial security. In this study, we introduce deep representation learning-based approaches relying mainly on large language models (LLMs) for identifying fraud in financial statements by examining temporal changes in the Management Discussion and Analysis (MD&amp;A) sections of corporate disclosures. Departing from conventional techniques that rely only on word frequency analysis, we propose D<span>eep</span>F<span>raud</span> that combines time-evolving financial LLM embeddings, such as FinBERT, FinLlama, and FinGPT embeddings, of paragraphs and uses long short-term memory (LSTM) to predict frauds via historical textual embeddings. In addition to LLM embeddings, we also integrate (1) time-evolving word frequencies of words relevant to fraud detection, such as those expressing sentiment or uncertainty, and (2) time-evolving financial ratios. Trajectories of paragraph-level embeddings, frequencies, and ratios are used to construct a fraud detection model, which we evaluate against machine learning methods and deep time-series models. Using 30 years of financial report data (from 1995 to 2024), our experiments demonstrate that D<span>eep</span>F<span>raud</span> on average enhances fraud detection performance across a number of scenarios and on average outperforms the competing approaches as well as conventional word frequency approaches. Our framework introduces a novel direction for deep feature engineering in the field of financial statement fraud detection.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695568","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}

{"title":"Identifying Going Concern Audit Opinions Using Supervised Machine Learning","authors":"Dennis Hedback","doi":"10.1002/isaf.70020","DOIUrl":"https://doi.org/10.1002/isaf.70020","url":null,"abstract":"<p>This paper evaluates the use of supervised machine learning to automatically identify going concern–modified audit reports. Models based on two different classifiers—logistic regression and extreme gradient boosting—achieve strong classification performance for this task. The same classifiers, along with naïve Bayes, also demonstrate strong performance in the ancillary task of identifying audit report pages in financial reports. These results have practical implications, including the application of the presented methods for timely accounting information retrieval for users, automated peer comparison for auditors, or as a data extraction method for researchers, particularly in settings with limited audit data availability.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316927","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}

{"title":"Balanced Underbagged Ensemble Approach for Classifying Highly Imbalanced Datasets in the Insurance and Financial Sectors","authors":"Alberto Gutierrez-Gallego,&nbsp;Oscar Garnica,&nbsp;Daniel Parra,&nbsp;J. Manuel Velasco,&nbsp;J. Ignacio Hidalgo","doi":"10.1002/isaf.70018","DOIUrl":"https://doi.org/10.1002/isaf.70018","url":null,"abstract":"<p>Data bias is a critical challenge in machine learning applications within the financial and insurance sectors, as it can lead to misleading risk assessments and inaccurate predictive models. A prevalent source of bias in real-world datasets is the imbalanced distribution of classes, which is particularly problematic in fraud detection, credit risk assessment, and claim prediction. Traditional approaches to handling imbalanced data often rely on undersampling or oversampling techniques. However, these methods may generate unrealistic minority class samples or fail to perform effectively when dealing with extreme class imbalances. In this paper, we propose a configurable technique based on the underbagging method, integrated with a classifier for highly imbalanced datasets. Our approach is designed to enhance the predictive accuracy of the minority class while maintaining robust performance for the majority class. We incorporate our methodology into a classification ensemble framework and evaluate its effectiveness by comparing it against 100 combinations of 10 different oversampling and undersampling techniques applied to 10 different machine learning algorithms. The evaluation is conducted on two highly imbalanced real-world datasets: one related to auto insurance claims and another focused on credit card fraud detection. Our statistical analysis demonstrates that Balanced Underbagged Ensemble achieves superior classification performance in terms of recall for both classes, regardless of the base machine learning model used within the ensemble. Furthermore, our method finds an optimal balance between classification performance and computational efficiency.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272973","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}

{"title":"Lost in the Modeling Stage: A Comparative Analysis of Machine Learning Models for Real Estate Data","authors":"Ian Lenaers,&nbsp;Lieven De Moor","doi":"10.1002/isaf.70019","DOIUrl":"https://doi.org/10.1002/isaf.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Machine learning dominates automated property valuation, yet comprehensive comparisons of predictive models remain scarce. This study compares 28 rent prediction models using 79,735 Belgian residential rental properties from 2022. Predictive performance is evaluated with traditional and alternative metrics for train data, test data, and across deciles. The results confirm that tree-based ensemble models outperform others, with stacking and averaging yielding superior results at a higher computational cost. Furthermore, middle-range rents show better predictive accuracy than extremes. Traditional and alternative metrics provide consistent findings. These insights aid real estate stakeholders seeking to enhance their expert systems for real estate price modeling.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272747","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}

{"title":"Understanding Decision-Making to Tackle Complexity in Open Innovation Labs in Government","authors":"Ben De Coninck,&nbsp;Stijn Viaene,&nbsp;Jan Leysen","doi":"10.1002/isaf.70017","DOIUrl":"https://doi.org/10.1002/isaf.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>This article examines the decision-making processes in open innovation labs (OI-labs) in government. Through a qualitative single case study, we explore how the use of causal and effectual reasoning, as dichotomous logics, evolves over time and is manifested in the form of organizational practices to tackle temporal, relational, and cultural complexity. The findings reveal three episodes: the conceptualizing of the lab (predominantly causation), the building of the lab (predominantly effectuation), and the sustaining of the lab (hybrid causation–effectuation). Moreover, shifts in the logic are aimed at addressing different types of complexity, and over time, a hybrid logic emerges.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038289","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}

{"title":"FinSentiment: Predicting Financial Sentiment Through Transfer Learning","authors":"Zehra Erva Ergun,&nbsp;Emre Sefer","doi":"10.1002/isaf.70015","DOIUrl":"https://doi.org/10.1002/isaf.70015","url":null,"abstract":"<div>\u0000 \u0000 <p>There is an increasing interest in financial text mining tasks. Significant progress has been made by using deep learning-based models on a generic corpus, which also shows reasonable results on financial text mining tasks such as financial sentiment analysis. However, financial sentiment analysis is still demanding work because of the insufficiency of labeled data for the financial domain and its specialized language. General-purpose deep learning methods are not as effective mainly due to specialized language used in the financial context. In this study, we focus on enhancing the performance of financial text mining tasks by improving the existing pretrained language models via NLP transfer learning. Pretrained language models demand a small quantity of labeled samples, and they could be enhanced to a greater extent by training them on domain-specific corpora instead. We propose an enhanced model FinSentiment, which incorporates enhanced versions of a number of recently proposed pretrained models, such as BERT, XLNet, RoBERTa, GPT, Llama, and T5, to better perform across NLP tasks in financial domain by training these models on financial domain corpora. The corresponding finance-specific models in FinSentiment are called Fin-BERT, Fin-XLNet, Fin-RoBERTa, Fin-GPT, Fin-Llama, and Fin-T5, respectively. We also propose variants of these models jointly trained over financial domain and general corpora. Our finance-specific FinSentiment models, in general, show the best performance across three financial sentiment analysis datasets, even when only a subpart of these models is fine-tuned with a smaller training set. Our results exhibit enhancement for each tested performance criteria on the existing results for these datasets. Extensive experimental results demonstrate the effectiveness and robustness of especially RoBERTa pretrained on financial corpora. Overall, we show that NLP transfer learning techniques are favorable solutions to financial sentiment analysis tasks. Our source code has been deposited at https://github.com/seferlab/finsentiment.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037646","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}