Topic Editors

Prof. Dr. Xinwei Cao
School of Business, Jiangnan University, Lihu Blvd, Wuxi 214122, China
Dr. Tran Thu Ha
Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi 000084, Vietnam
School of Mathematical Sciences, Tongji University, Shanghai, China
Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Zografou, Greece
Department of Land Surveying and Geo-Informatics, Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong, China
Prof. Dr. Shuai Li
Faculty of Information Technology and Electrical Engineering (ITEE), University of Oulu, Oulu, Finland

Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications

Abstract submission deadline
29 February 2024
Manuscript submission deadline
31 May 2024
Viewed by
4662

Topic Information

Dear Colleagues,

In recent years, we have witnessed a paradigm shift in various sectors, engendered by the profound transformation induced by artificial intelligence (AI) and machine learning (ML). These rapidly evolving fields have inscribed a new narrative across a multitude of industries, carving out novel frontiers in the process. Among the disciplines touched by this technological revolution, accounting and finance stand out as pivotal beneficiaries. AI and ML, with their robust computational capabilities, have been progressively integrated into a range of applications within the realms of accounting and finance. Their potential has been harnessed to revolutionize financial modeling, transforming it from a traditional statistical exercise into a dynamic and adaptable mechanism. Furthermore, risk management has been enhanced significantly, evolving from relying on historical data to predictive systems capable of anticipating and responding to possible future scenarios. In the auditing domain, AI and ML have introduced automation and advanced data analytics, thus enabling more comprehensive, accurate, and efficient audit processes. Moreover, these technologies are becoming instrumental in ensuring regulatory compliance, as they are capable of continuously monitoring and assessing vast amounts of data against ever-changing regulatory requirements. Their contribution extends beyond improving efficiency and accuracy; they provide unprecedented insights that are transforming decision-making processes. With this backdrop, we strongly encourage researchers to submit their scholarly work that illuminates the varied applications and impacts of AI and ML within the domain of accounting and finance. The aim is to foster a nuanced understanding of how these advanced technologies are reshaping the landscape of these fields, and the broader implications for organizations, industry professionals, and policymakers. Potential topics include, but are not limited to:

  • Application of AI and ML in financial decision-making
  • ML based portfolio optimization for risk management
  • Financial fraud detection based on big data analytics
  • Predictive models in finance and accounting using AI and ML
  • Role of AI and ML in risk management and auditing
  • Integration of AI and ML with blockchain technologies in accounting and finance
  • AI and ML in financial services and fintech
  • Ethical, social, and regulatory implications of AI and ML in accounting and finance
  • Case studies showcasing successful applications of AI and ML in the accounting and finance sector

Authors are expected to contribute to the body of knowledge in the field by presenting novel theoretical perspectives, innovative methodologies, or empirical findings. Submissions that offer insights into the practical implications for industry professionals and policymakers are also highly desirable.

Prof. Dr. Xinwei Cao
Dr. Tran Thu Ha
Prof. Dr. Dunhui Xiao
Dr. Vasilios N. Katsikis
Dr. Ameer Hamza Khan
Prof. Dr. Shuai Li
Topic Editors

Keywords

  • AI
  • machine learning
  • accounting
  • finance
  • risk management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
- - 2020 20.8 Days CHF 1600 Submit
Computation
computation
2.2 3.3 2013 18 Days CHF 1800 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Risks
risks
2.2 3.1 2013 20.4 Days CHF 1800 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (4 papers)

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26 pages, 6219 KiB  
Article
Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk
Risks 2024, 12(2), 31; https://doi.org/10.3390/risks12020031 - 03 Feb 2024
Viewed by 547
Abstract
Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the [...] Read more.
Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk. Full article
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19 pages, 397 KiB  
Review
Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review
Mathematics 2023, 11(24), 4943; https://doi.org/10.3390/math11244943 - 13 Dec 2023
Viewed by 860
Abstract
This paper reviews 17 studies addressing dynamic option hedging in frictional markets through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL models, state and action spaces, reward formulations, data generation processes and results for each study. It is found that policy [...] Read more.
This paper reviews 17 studies addressing dynamic option hedging in frictional markets through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL models, state and action spaces, reward formulations, data generation processes and results for each study. It is found that policy methods such as DDPG are more commonly employed due to their suitability for continuous action spaces. Despite diverse state space definitions, a lack of consensus exists on variable inclusion, prompting a call for thorough sensitivity analyses. Mean-variance metrics prevail in reward formulations, with episodic return, VaR and CvaR also yielding comparable results. Geometric Brownian motion is the primary data generation process, supplemented by stochastic volatility models like SABR (stochastic alpha, beta, rho) and the Heston model. RL agents, particularly those monitoring transaction costs, consistently outperform the Black–Scholes Delta method in frictional environments. Although consistent results emerge under constant and stochastic volatility scenarios, variations arise when employing real data. The lack of a standardized testing dataset or universal benchmark in the RL hedging space makes it difficult to compare results across different studies. A recommended future direction for this work is an implementation of DRL for hedging American options and an investigation of how DRL performs compared to other numerical American option hedging methods. Full article
13 pages, 323 KiB  
Article
Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence
Sustainability 2023, 15(23), 16482; https://doi.org/10.3390/su152316482 - 01 Dec 2023
Viewed by 579
Abstract
Understanding business failure within the transport industry is crucial for formulating an effective competitive policy. Acknowledging the pivotal role of financial stability as a cornerstone of sustainability, this study undertakes a comparative investigation between statistical models forecasting business failure and artificial intelligence-based models [...] Read more.
Understanding business failure within the transport industry is crucial for formulating an effective competitive policy. Acknowledging the pivotal role of financial stability as a cornerstone of sustainability, this study undertakes a comparative investigation between statistical models forecasting business failure and artificial intelligence-based models within the context of the transport sector. The analysis spans the temporal period from 2014 to 2021 and encompasses a dataset of 4866 companies from four South European countries: Portugal, Spain, France, and Italy. The models created were linear support vector machines (L-SVMs), kernel support vector machines (K-SVMs), k-nearest neighbors (k-NNs), logistic regression (LR), decision trees (DTs), random forests (RFs), extremely random forests (ERFs), AdaBoost, and neural networks (NNs). The models were implemented in Python using the scikit-learn package. The results revealed that most models exhibited high precision and accuracy, ranging from 71% to 73%, with the ERF model outperforming others in both predictive capacity and accuracy. It was also observed that artificial intelligence-based models outperformed statistical models in predicting business failure, with particular emphasis on the AdaBoost and ERF models. Thus, we conclude that the results confirm the hypothesis that the artificial intelligence models were superior in all metrics compared to the results obtained by logistic regression. Full article
14 pages, 792 KiB  
Article
The Role of Internal Auditing in Improving the Accounting Information System in Jordanian Banks by Using Organizational Commitment as a Mediator
Risks 2023, 11(9), 153; https://doi.org/10.3390/risks11090153 - 25 Aug 2023
Cited by 3 | Viewed by 1737
Abstract
In light of the function of Internal Auditing and its significance in assessing and ensuring the validity of data, information, reports, and high lists generated by the Accounting Information System and improving its credibility and dependability, the purpose of this study was to [...] Read more.
In light of the function of Internal Auditing and its significance in assessing and ensuring the validity of data, information, reports, and high lists generated by the Accounting Information System and improving its credibility and dependability, the purpose of this study was to investigate the relationship between Internal Auditing (IA) and Accounting Information System (AIS) in Jordanian banks, with a focus on the mediator role of Organizational Commitment (OC). A cross-sectional survey method was used to collect data from a sample of employees who work in banks, including those who work in the internal audit department. The collected data were analyzed using SPSS 26.0 and PROCESS V4.1. The study sample includes 193 employees who work in banks, including those who work in the internal audit department. Descriptive statistical methods, such as frequencies, percentages, means, and standard deviations, were employed to depict both the characteristics of the sample and the participants’ responses to the study items. The results indicate that IA has a positive relationship with AIS. Moreover, the results indicate that OC partially mediates the relationship between IA and AIS in Jordanian banks. Full article
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