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

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

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 (5 papers)

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All AI Computation Mathematics Risks Sustainability

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27 pages, 4289 KiB  

Open AccessArticle

Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents’ Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling

by Chengzhe Lyu

Sustainability 2024, 16(8), 3280; https://doi.org/10.3390/su16083280 - 15 Apr 2024

Viewed by 381

Abstract

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how [...] Read more.

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how the design and management of urban spaces can encourage pro-environmental attitudes and actions among the population. The study specifically focuses on dynamic indicators, namely vitality, accessibility, quality, and walkability, to develop a comprehensive understanding of the utilization and experience of urban spaces. The research outcomes will make valuable contributions towards Shanghai’s objectives of achieving environmental sustainability, while also providing insights that have broader relevance to sustainable urban development globally. As the built environment significantly influences energy consumption, resource utilization, pollution generation, and overall human wellbeing, attaining urban sustainability is of paramount importance. The findings of this study will aid in informing strategies and policies that promote sustainable practices, not only benefiting Shanghai but also serving as a valuable resource for urban development initiatives worldwide. Sustainable urban design principles, including compactness, density, mixed land use, greening, and walkability, have been associated with pro-environmental behaviors, including reduced reliance on automobiles, increased walking and cycling, and heightened environmental consciousness. Nevertheless, the relationship between the built environment and sustainability behaviors is intricate and is influenced by multiple factors. Consequently, further research is necessary to comprehend how specific spatial and temporal dynamics impact environmental behaviors within urban settings. In this study, an artificial neural network (ANN) was developed to estimate the quality and walkability of an area and environmental behaviors by considering the augmented vitality and accessibility factors. The ANN’s predictions demonstrate that higher levels of vitality and accessibility positively contribute to improved walkability and environmental behaviors. The accuracy of the ANN’s predictions was assessed using linear regression, which yielded acceptable error rates when compared with experimental results. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications)

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26 pages, 6219 KiB  

Open AccessArticle

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk

by Hao Wang, Anthony Bellotti, Rong Qu and Ruibin Bai

Risks 2024, 12(2), 31; https://doi.org/10.3390/risks12020031 - 03 Feb 2024

Viewed by 1318

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

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications)

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19 pages, 397 KiB  

Open AccessReview

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

by Reilly Pickard and Yuri Lawryshyn

Mathematics 2023, 11(24), 4943; https://doi.org/10.3390/math11244943 - 13 Dec 2023

Viewed by 1208

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

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications)

13 pages, 323 KiB  

Open AccessArticle

Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

by Amélia Ferreira da Silva, José Henrique Brito, Mariline Lourenço and José Manuel Pereira

Sustainability 2023, 15(23), 16482; https://doi.org/10.3390/su152316482 - 01 Dec 2023

Viewed by 786

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

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications)

14 pages, 792 KiB  

Open AccessArticle

The Role of Internal Auditing in Improving the Accounting Information System in Jordanian Banks by Using Organizational Commitment as a Mediator

by Mo’taz Kamel Al Zobi and Baker Akram Falah Jarah

Risks 2023, 11(9), 153; https://doi.org/10.3390/risks11090153 - 25 Aug 2023

Cited by 3 | Viewed by 2160

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

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Accounting and Finance: Theories and Applications)

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