Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study
1
Department of Regional and Economic Development, School of Applied Economics and Social Sciences, Agricultural University of Athens, New Building-New City, GR33100 Amfissa, Greece
2
Department of Agribusiness & Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 1st km of Old National Road Thebes-Elefsis, GR32200 Thebes, Greece
*
Author to whom correspondence should be addressed.
Computation 2025, 13(7), 172; https://doi.org/10.3390/computation13070172
Submission received: 16 June 2025
/
Revised: 10 July 2025
/
Accepted: 14 July 2025
/
Published: 17 July 2025
(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)
Abstract
Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed bibliometric analysis of the literature sourced from Web of Science from 2000 to 2025. A comprehensive search in the Web of Science (WoS) Core Collection yielded 1083 peer-reviewed publications, which we analyzed using analytical tools like VOSviewer 1.6.20 and Bibliometrix (Biblioshiny 5.0) so as to examine the dataset and uncover bibliometric characteristics like citation patterns, keyword occurrences, and thematic clustering. Our initial analysis results uncover the presence of key research clusters focusing on bankruptcy prediction, AI integration in financial services, and advanced deep learning applications. Furthermore, our findings note a transition of CRA from an emerging to an expanding domain, especially after 2019, with terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) being identified as prominent keywords and a recent shift towards blockchain, explainability, and financial stability being present. We believe that this study tries to address the need for an updated mapping of CRA, providing valuable insights for future academic inquiry and practical financial risk management applications.
1. Introduction
In academia, it has been well documented over the last century that financial institutions must manage various risks, including credit, market, operational, and systemic risks. Since the first quarter of the 21st century, mainly due to globalization and the complexity of new financial instruments and transactions, it is believed that the banking sector has faced increasing complexity.
It has also been shown that the traditional risk assessment models are not always adequate to handle modern financial data, which has more complex structures and characteristics. In response to the new challenges of the 21st century, research in new technologies utilizing CRA has emerged. The term includes technologies like AI and ML, which can be further analyzed into clusters like data mining (DM), artificial neural networks (ANNs), computational intelligence (CI), operational research (OR), and big data analytics (BDA).
As has also been shown, adopting CRA in banking and finance offers advantages that lead to better financial stability, processing efficiency, and thus competitiveness [1,2,3]. On the other hand, these tools raise concerns about transparency, ethical use, data privacy, and regulatory compliance [4].
Due to the previous work of many researchers, we can spot multiple research streams in the CRA field, including credit risk modeling, corporate bankruptcy prediction, fraud detection, operational efficiency, and Financial Technology (FinTech) innovations [5,6,7]. Over the last decade, academia has produced systematic literature reviews and bibliometric analyses to synthesize the overall research developments and identify emerging trends [8,9,10].
Among early influential reviews, Ref. [5] systematically classified DM methods for financial fraud detection. Additionally, at the early stage of the last two decades of reviews, Ref. [3] published a comparative survey of AI applications in finance, which highlighted ANNs, expert systems, and hybrid models across many financial domains. In another area of research, that of banking efficiency, Ref. [1] approached the research with a bibliometric review of operational research and AI methods applied to bank performance, justifying the relevance of hybrid AI models for operational risk assessment.
Other prominent topics that this preliminary study of the relevant reviews to date has revealed are corporate failure and bankruptcy prediction. Ref. [11] did a comparison of multiple ML models in bankruptcy prediction and found that ensemble methods often outperform traditional statistical models. Ref. [9] emphasized challenges related to interpretability and generalization by systematically reviewing corporate default prediction models. Another relatively recent review by [4] highlighted debates on sampling techniques, model evaluation, and data quality by synthesizing developments in bankruptcy prediction.
FinTech innovations have introduced additional dimensions to CRA research. Ref. [12] reviewed ML-based digital credit scoring models applied to finance, demonstrating how AI supports financial inclusion. More recently, Ref. [13] investigated BDA and Internet of Things (IoT)-based FinTech solutions, emphasizing how these technologies reshape financial services while raising new regulatory concerns. Ref. [14] examined the convergence of AI, cloud computing, and blockchain technologies, identifying opportunities for efficiency and risks related to decentralization. Ref. [15] conducted a comprehensive review of AI-based credit risk assessment, summarizing supervised and unsupervised learning approaches and emphasizing feature selection, model validation, and ethical deployment challenges.
Based on the relevant research and a first comparison on bibliometric analysis and reviews in the field, as shown in Table 1, we believe that many prior reviews have focused relatively narrowly on specific CRA applications or computational techniques, often using limited datasets. This fragmented approach limits the integration of CRA subfields and fails to fully reflect the accelerated expansion of CRA research in recent years. Moreover, few studies adopt comprehensive bibliometric frameworks to systematically map the intellectual structure of CRA research across multiple subdomains.
To address these gaps, we present a comprehensive bibliometric analysis of CRA research in banking. Using data from the WoS Core Collection, a structured search query was applied, retrieving 2692 records. Following filtering based on publication years (2000–2025), subject areas, document types, and language criteria, 1083 peer-reviewed articles from 383 academic journals were retained, as listed in Table A1 of Appendix A. From these, 62 review articles were extracted for detailed analysis.
The following research questions guided our review:
- RQ1: What are the publication trends in CRA research?
- RQ2: Where are the most influential publications (outlets and articles) on CRA?
- RQ3: Who are the most prolific contributors to CRA research (authors, countries, and institutions)?
- RQ4: What insights do existing research themes and topics provide about CRA?
- RQ5: Has CRA evolved into a multidisciplinary research field bridging computer science, finance, and decision sciences?
- RQ6: What future research avenues can be explored to enrich our understanding of CRA?
We attempt to take a new approach compared to earlier reviews by combining different areas of CRA, like credit risk, bankruptcy prediction, fraud detection, operational efficiency, and FinTech innovations, into a single bibliometric framework. The analysis employs VOSviewer for co-citation and thematic clustering [16] and Bibliometrix in RStudio 4.5.0 for performance analysis (Ref. [17]). The findings provide an updated, reproducible, and comprehensive mapping of CRA research, offering insights into future academic inquiry and practical financial risk management applications.
In order to optimize the applications and benefits of a dynamic financial landscape, further research is needed to address the growing academic interest. This paper has the following structure: Section 2 describes the bibliometric methodology employed and the data collected. Section 3 presents the results. Section 4 provides a comprehensive discussion, outlines implications for academic research, and concluding remarks.
2. Materials and Methods
2.1. Data Collection
2.1.1. The Source of Data Collection
The WoS database was selected for the data collection due to its extensive coverage of high-quality peer-reviewed journals and its robustness for bibliometric analysis. WoS is renowned for its systematic indexing, multidisciplinary orientation, and comprehensive citation tracking, ensuring the accuracy and reliability of bibliometric analyses [18]. While this study relies on the Web of Science Core Collection for its bibliometric analysis, we acknowledge that Scopus and Google Scholar may index additional journals relevant to CRA, including practitioner-oriented outlets. Future research could incorporate multiple databases to achieve broader coverage.
2.1.2. Keyword Selection Strategy and Refinement Process
The keyword selection process was carried out in two main phases to ensure accuracy. In phase one, we examined CI and its further synonyms or analytical terms, banking and finance, and their synonyms or derivative words in online dictionaries to select the most relevant keywords. In phase two, we employed a WoS search for CI terminologies and AI, ML, ANN, banking and finance, credit risk, systemic risk, etc. After refining the query, the search was conducted based on specific inclusion criteria, detailed in Table 1.
While the generic term ‘risk’ covers a broader array of topics, we refined our search to specific risk categories most relevant to computational applications in banking to improve precision and avoid retrieving unrelated research. Future studies might consider broader or alternative keyword combinations, including terms like ‘idiosyncratic risk,’ to explore additional research niches within CRA
The extensive preliminary dataset required refinement to increase relevance and specificity. First, we narrowed the publication period to articles from 2000 to 2025, justified by the limited number of financial CRA studies before 2000. Second, the search was further filtered to include only relevant journals from economics, finance, business, operations research, computer science, engineering, and multidisciplinary fields. Third, we restricted the results to articles published in English-language journals only. Lastly, we included peer-reviewed research articles and reviews, resulting in a refined dataset of 1083 records.
Table 2 summarizes the step-by-step filtering approach and corresponding outcomes.
2.2. Study Approach and Tools
The bibliometric approach, introduced initially by [19], systematically analyzes the networks of scholarly literature based on citation patterns. This methodology has gained extensive adoption for its ability to produce accurate, transparent, and reproducible results, providing comprehensive insights into intellectual advancements within research domains [20]. Furthermore, bibliometrics effectively explores various dimensions, including journals, topics, authors, institutions, and geographical research distributions [21]. When combined with content analysis, bibliometric methods allow the summarization of contributions to a research field, providing valuable directions for future research.
VOSviewer, developed by [16], is a software tool for constructing and visualizing bibliometric networks, enabling analyses such as co-authorship (to reveal collaboration patterns), co-citation (to identify intellectual foundations of a field), bibliographic coupling (to group documents sharing similar references), and keyword co-occurrence (to discover thematic structures and emerging research trends). These analyses collectively help uncover the intellectual, social, and conceptual landscapes of CRA research. Moreover, the Bibliometrix software developed by [17] and accessed via its user-friendly Biblioshiny interface in RStudio provided an interactive interface that facilitated performance analysis by generating descriptive statistics on publication trends, authorship patterns, and citation metrics. This provides an overview of the productivity and impact dimensions of CRA research. Collectively, these computational tools provided a robust methodological framework for comprehensive science mapping analysis and thematic exploration of the CRA banking literature.
3. Results
3.1. Dataset General Information
Table 3 presents preliminary data and basic metrics. 1083 articles were analyzed, with an average of 19.9 citations per document. These articles used 2906 author keywords and featured 2952 authors, 103 of whom were single authors. The annual growth rate in the CRA domain was 21.64%, with a collaboration index of 3.13 and international co-authorship at 30.38%.
3.2. Publication Trend: A Temporal Analysis (RQ1)
Between 2000 and 2010, CRA-related studies remained minimal, averaging fewer than five articles per year, as illustrated in Figure 1. From 2011 to 2019, output increased gradually, reaching 58 papers by 2019. A significant surge followed, peaking at 143 articles in 2023. The compound annual growth rate for 2000–2025 was 21.64%, highlighting CRA’s transition from an emerging field to a structured and expanding research domain.
3.3. Publication Outlets (RQ2)
The journal Applied Soft Computing has the highest number of published articles (25), followed by Computational Economics (24), the European Journal of Operational Research (24), Risks (23), and the Annals of Operations Research (21). Furthermore, Table 4 lists the top journals by total citations, also including Scopus and WoS rankings, providing valuable insights for researchers seeking high-impact sources in CRA research.
The most cited sources in terms of total citations (TC) are Decision Support Systems (2009), the European Journal of Operational Research (1925), Applied Soft Computing (1603), Knowledge-Based Systems (1524), Neural Computing & Applications (937), and the International Journal of Bank Marketing (513). These journals have been instrumental in advancing research at the intersection of computational intelligence and banking risk analytics, consistently publishing influential studies on algorithmic modeling, predictive analytics, and data-driven financial decision-making.
3.4. Globally Cited Articles (RQ2)
Table 5 presents the most globally cited articles on CRA in banking, providing valuable insights into the most impactful research in this field. The most cited article, by [5] in Decision Support Systems, highlights the application of DM techniques for financial fraud detection through a structured classification framework and comprehensive literature review.
Another highly cited paper by [22] focuses on predicting the success of bank telemarketing using a data-driven approach. The study presents a robust predictive model built on real-world banking campaign data. Ref. [1] investigated OR and AI techniques to assess bank efficiency and performance. Ref. [2] examined consumer credit risk models utilizing machine-learning algorithms to enhance prediction accuracy. Ref. [3] found that a comparative analysis of ANN, expert systems, and hybrid intelligent systems provides meaningful insights into AI’s role in financial applications. Ref. [7] examined financial distress prediction among Chinese listed companies, highlighting the effectiveness of DM techniques. Ref. [8] offered a comprehensive review of state-of-the-art methods in corporate failure prediction. These influential articles contribute significantly to developing computational frameworks and data-centric methodologies in banking risk analytics.
3.5. Prolific Contributors, Authors (RQ3)
Table 6 lists the ten most productive authors in CRA research, along with their respective affiliations, countries, total publications (TP), total citations (TC), and the starting year of their publications (PY_start). The authors represent a diverse geographical spread, including China, Taiwan, France, Hungary, the UK, Portugal, and Greece.
Sun Jie, affiliated with Harbin Institute of Technology in China, leads in TP with nine, accumulating 646 TC. Li Hui, also from China, matches Sun Jie’s citations with eight publications. Tsai CF from Taiwan’s National Central University stands out with the highest TC (843) from eight publications, starting in 2009.
Shi Yong (China), Du Jardin Philippe (France), and Virág Miklós (Hungary) each have five publications, with varying citation counts. Liang Deron (Taiwan), Mues Christophe (UK), Ribeiro Bernardete (Portugal), and Petropoulos Anastasios (Greece) have four publications, with their starting years ranging from 2012 to 2020, indicating more recent contributions for some. The data generally suggest a correlation between higher publications and higher citations, though with notable exceptions like Tsai CF’s impact.
3.6. Prolific Contributors, Countries, and Institutions (RQ3)
3.6.1. Countries’ Scientific Production and Most-Cited Countries
Table 7 provides an overview of the top 10 corresponding authors’ countries in the dataset, listing the number of articles authored through single-country (SCP) and multi-country (MCP) collaborations. The top five countries by total article count are China (212), India (109), the United States (77), the United Kingdom (51), and Spain (46). China (53), the United Kingdom (31), the United States (29), Italy (17), and India (15) exhibit the highest levels of international collaboration in CRA research, as indicated by their MCP values. For relative terms, as a percentage of their TP, we observe a different distribution with the UK (60.78%) coming first, followed by Italy (45.95%), the USA (37,66%), France (33.33%), and Australia (28%), with China and India only coming ahead of Iran, which can be interpreted as small level of international co-authored publications in these countries. Nevertheless, these findings highlight the prominent geographic distribution and underscore the collaborative dynamics driving the global advancement of computational risk analytics in banking.
3.6.2. Affiliations’ Scientific Output
In Table 8, we illustrate scientific output in CRA research of the leading ten affiliations to offer insights into global productivity and historical engagement. Islamic Azad University (Iran) exhibits preeminence in both recent (25 articles in 2025) and cumulative (108 total articles) publication volume, having an initial year of production in 2013. Its articles per year rate (8.3) is surpassed by the Ministry of Education and Science (Ukraine) and the University of London, which demonstrate higher annual outputs of 9.8 and 8.8, respectively, despite both entering seven years later (2020). The geographical distribution underscores a diverse international research landscape, encompassing Asian, European, and African institutions.
Notably, the National Central University (Taiwan) distinguishes itself with the highest total article count (141) and the earliest recorded first production year (2009), signaling a sustained, long-term commitment. Including governmental entities, such as the European Central Bank, alongside academic institutions highlight varied organizational contributions to CRA research. Furthermore, the prominent presence of institutions with relatively recent first-production years (e.g., University of London, 2020; University of Granada, 2019) suggests a rapid intensification of their research efforts in CRA, swiftly elevating them to top-tier status.
3.7. Intellectual Structures of CRA Research RQ4
Moving on from descriptive analysis of the CRA bibliometric data, we continue to analyze more intellectual structures of the field. To do that, we transition to bibliometric research with VOSviewer, a more sophisticated tool for further employing strategies like co-authorship analysis, keyword co-occurrence analysis, bibliographic coupling, and co-citation analysis. Each of these analyses was configured using fractional counting, with specific thresholds for inclusion as reported below. The combination of these methods ensures a multidimensional understanding of the knowledge landscape in computational intelligence and risk management research in financial institutions.
3.7.1. Co-Authorship (Author Collaboration Networks)
Author collaboration networks or co-authorship, as frequently used, analyze the collaboration patterns among authors based on joint publications, with the intention to reveal research communities and leading contributors within the fields of CRA. Our co-authorship analysis was conducted using VOSviewer 1.6.20. To have the broadest spectrum of results, we applied no minimum requirements regarding the number of publications and citations. Out of 2952 authors, 60 were connected, resulting in nine distinct collaboration clusters, as shown in Figure 2.
The most collaborative authors in each cluster were determined by using a hierarchical ranking based on the number of collaboration links, total link strength, number of publications, and citation count. Drawing on the initial dataset, together with co-occurrence maps and thematic overlays, each cluster was also assigned a dominant indicative research subfield as illustrated in Table 9.
3.7.2. Keyword Co-Occurrence (DE—Author Keywords and ID—Keywords Plus)
Keyword co-occurrence examines how frequently author-defined or algorithmically generated (WoS Keywords Plus) keywords appear together in the same documents, thus identifying dominant themes and emerging topics directly or indirectly emphasized by the researchers.
- Keyword Co-occurrence (DE—Author Keywords)
To identify the thematic structure of research in computational risk analytics (CRA) in banking, a co-occurrence analysis of author keywords (DE) was conducted using VOSviewer. The threshold was set at a minimum of five occurrences, resulting in 121 keywords, of which 117 were connected and visualized in the network. These were grouped into 12 clusters based on their co-occurrence link strength, forming a detailed thematic map as illustrated in Figure 3. Keyword co-occurrence (DE—author keywords) network.
The most frequent keywords included ML (296 occurrences), AI (157), DL (73), bankruptcy prediction (52), credit risk (41), and financial distress (40). We believe that although bankruptcy and financial distress may seem closely related and are sometimes used interchangeably in the literature, they should be maintained as separate keywords in our analysis. This is because financial distress generally refers to the early stages of financial trouble, with potential avenues for recovery, whereas bankruptcy represents a formal legal process with more severe consequences. Furthermore, in banking research, this distinction is significant because it reflects different levels of systemic risk, regulatory interventions, and modeling approaches in CRA studies.
Each cluster represents a distinct thematic domain:
- Cluster 1 (red) focuses on ML and hybrid models in bankruptcy and credit default prediction, including significant words like ensemble, DT, and Support Vector Machine (SVM);
- Cluster 2 (green) concentrates on AI integration in financial services, featuring keywords like FinTech, digital banking, financial inclusion, and blockchain;
- Cluster 3 (blue) represents advanced deep learning applications in textual data, ESG, and credit analytics;
- Cluster 4 (yellow) captures traditional econometric and scoring methods, including Altman Z-score, logit model, and financial ratios.
The keyword network seen in Figure 3 shows a well-structured and maturing research field, with clear thematic boundaries and growing interdisciplinary integration between finance, computing, and decision sciences.
As far as temporal evolution is concerned (Figure 4), the overlay scores by average publication year indicate an evolution of focus toward more recent concepts such as Extreme Gradient Boosting (XGBoost), blockchain, explainability, and financial stability, especially post-2022.
This reflects a shift from traditional statistical modeling to interpretable, robust AI frameworks. Average normalized citation scores also highlight impactful keywords like DL (1.74), financial stability (1.68), and ensemble (1.06).
- Keyword Co-occurrence (ID—Keywords Plus)
Using VOSviewer to continue complementing the DE keyword analysis, a co-occurrence network of Keywords Plus (ID) was generated. With a minimum occurrence threshold of 5, the final map includes 151 terms across 12 clusters, as visualized in Figure 5. Keywords Plus, being algorithmically extracted from titles and cited references, offers a broader and more systemic view of thematic connections in the CRA-related literature.
The most frequently occurring terms were bankruptcy (227 occurrences), models (218), classification (146), credit risk (41), and financial distress (68).
As we can observe in Figure 5, each cluster group is related to terms that reflect conceptual trends:
- Cluster 1 (bankruptcy, models, classification) maps the computational core of CRA methods, such as default prediction, ensemble models, and ANNs;
- Cluster 2 (banking, impact, management) focuses on financial services and adoption frameworks, including FinTech, information systems, and technology acceptance models;
- Cluster 3 contains structural topics like corporate governance, performance, and corporate social responsibility (CSR), indicating interest in firm-level risk and disclosure;
- Other clusters cover the rest of the areas, such as macroeconomic instability (crisis, monetary policy); sustainability and ESG terms; and computational enhancements like optimization, feature selection, and survival analysis.
As far as temporal evolution is concerned (Figure 6), the temporal evolution of keyword co-occurrence (ID—Keywords Plus) network and overlay scores indicate that recent attention has shifted to topics such as blockchain, big data, internet banking, explainability, and sustainability, especially post-2022.
Keywords with high normalized citation scores include acceptance model (2.74), information technology (1.71), and fraud (1.77), revealing impactful and, at the same time, emerging themes.
This co-occurrence map, built on Keywords Plus, is an integrator of how the field of CRA in banking connects technical sophistication with sector-specific relevance and policy considerations.
3.7.3. Bibliographic Coupling (Documents)
Bibliographic coupling measures the similarity between documents based on shared references to detect groups of publications that cite similar literature.
The bibliographic coupling analysis reveals how documents are intellectually connected based on shared references. Applying a minimum citation threshold of 40, the analysis included 138 documents grouped into seven clusters, as shown in Figure 7. This approach identifies clusters of articles that, despite differing in methods or focus, build upon similar bodies of literature.
Each cluster represents a tightly coupled thematic stream.
- Cluster 1 (Red): Bankruptcy Prediction and Hybrid Methodologies
This cluster, having 298 total link strength (TLS), is primarily characterized by research focusing on bankruptcy prediction, often employing advanced techniques such as hybrid models and AI. Documents in this cluster explore various approaches to improve the accuracy and robustness of bankruptcy prediction models. For instance, Ref. [8] investigates the application of hybrid intelligent models for corporate financial distress prediction. Ref. [24] delves into DL models specifically designed for bankruptcy prediction, leveraging textual disclosures. Furthermore, Ref. [25] contributes to this theme by examining corporate bankruptcy prediction using an ANN and decision tree (DT) framework. The cluster also features work on intelligent techniques for financial forecasting, as seen in [26], which reviews AI in financial distress prediction.
- Cluster 2 (Green): Advanced ML for Credit Scoring and Financial Distress
With 417 TLS, this is a prominent and highly active cluster, characterized by the application of advanced machine learning techniques, particularly ensemble methods, to credit scoring and financial distress prediction. It features works exploring various algorithms such as ANN, random forests (RF), gradient boosting (GB), and SVM, exemplified by [3], which provides a comprehensive survey of AI applications in financial distress prediction, and [27], which investigates ML methods for credit scoring. A significant sub-theme is the optimization and hybridization of these models for improved predictive accuracy and robustness, often addressing issues like imbalanced datasets and feature selection in complex financial data, as explored by [28] in a review of the credit scoring literature and [29] in their review of financial credit risk assessment.
- Cluster 3 (Blue): AI and FinTech in Finance
The blue cluster comes third with 146 TLS and focuses on the intersection of AI and FinTech, exploring their transformative impact on various aspects of finance. Research within this cluster examines the integration of AI algorithms and machine learning into financial services, particularly in areas like credit risk assessment and digital transformation. For example, Ref. [30] explores how AI transforms relationships in the financial services industry. Ref. [31] proposes a digital servitization value co-creation framework for AI services, outlining a research agenda for digital transformation in financial service ecosystems. Ref. [32] discusses AI and the digital transformation of financial services, while [33] provides a survey of AI techniques in finance and financial markets.
- Cluster 4 (Yellow): DM and ML in Financial Fraud and Risk
This cluster has 104 TLS and highlights the application of DM and ML techniques for detecting and managing various forms of financial risk, including fraud and systemic risk. The works in this cluster emphasize using computational methods to identify patterns and anomalies in financial data. Ref. [5] provides a comprehensive survey of DM applications in financial fraud detection. Ref. [34] focuses on ML methods for systemic risk analysis within financial sectors. Additionally, [35] offer a review of the literature on DM applications in accounting, providing an organizing framework for the field.
- Cluster 5 (Purple): Feature Engineering and Model Enhancement for Bankruptcy Prediction
In terms of TLS (128), purple is the fourth cluster that specifically concentrates on enhancing the performance of bankruptcy prediction models through advanced techniques such as feature engineering and robust model development, often addressing issues like imbalanced datasets. Ref. [36] conducts a comprehensive study on the role of financial ratios and corporate governance indicators in bankruptcy prediction. Ref. [37] introduces a novel two-stage hybrid financial distress prediction model. The application of sophisticated algorithms to improve predictive accuracy is also a key theme, as seen in [38], which explores AI for credit scoring using financial and non-financial information.
- Cluster 6 (Turquoise): Credit Risk Management and Loan Defaults
The 107 TLS turquoise cluster is dedicated to research on credit risk management, with a particular emphasis on understanding and predicting loan defaults within the financial industry. The studies here often analyze factors contributing to credit risk and evaluate methodologies for its mitigation. Ref. [1] provides a comprehensive literature review of credit scoring models using evolutionary algorithms. Ref. [39] investigates the determinants of mortgage defaults. Furthermore, Ref. [40] examines risk and risk management specifically within the credit card industry, while [41] focuses on building a hybrid model for credit risk evaluation using credit card data.
- Cluster 7 (Orange): Predicting Bank Insolvencies and FinTech’s Impact on Stability
Finally, with 64 TLS, this cluster primarily focuses on the prediction of bank insolvencies and the broader implications of FinTech on financial stability. Research in this area utilizes various predictive models to anticipate bank distress and explores how the advent of FinTech influences credit risk and overall financial system resilience. Ref. [42] examines predicting bank insolvencies using machine learning techniques. Ref. [43] contributes to this by anticipating bank distress in the Eurozone using an XGBoost approach. Additionally, Ref. [23] investigates whether bank FinTech reduces credit risk, providing evidence from China, while [44] discusses the relationship between banking and information technology, highlighting the role of AI and FinTech.
The overlay visualization by average publication year illustrated in Figure 8, shows that clusters related to fairness, explainability, FinTech integration, and ESG risk have gained traction after 2020.
Meanwhile, as illustrated in Figure 9, the citation overlay map of bibliographic coupling in CRA documents emphasizes that foundational machine learning research in credit and default prediction continues to be heavily referenced.
Interpreting the most cited articles with the help of these structures, we observe how CRA in banking has evolved from technical algorithm development toward broader financial, technological, and social applications.
3.7.4. Co-Citation (Cited References)
Co-citation analyzes how often pairs of references are cited together across the dataset, aiming to reveal foundational works and the intellectual base of the CRA in the banking field.
The co-citation analysis identifies foundational literature in the field of CRA in banking by examining how frequently references are cited together across documents. Applying a threshold of thirty minimum co-citations, the analysis included fifty-nine references grouped into five intellectual clusters. The results are visualized in Figure 10.
Key findings per cluster:
- Cluster 1 (red): Foundational Financial Distress Prediction Models. In this cluster, prominently featuring references such as [45,46], Refs. [47,48] epresent the foundational theories and models in financial distress prediction, notably including the Z-score and O-score models. These works are central to the field, evidenced by their high citation counts and substantial inter-cluster links, indicating their pervasive influence across various research streams. References like [49,50] also reinforce this cluster’s focus on early quantitative approaches to corporate solvency assessment. Its high density and central positioning in the network images of Figure 10 underscore its role as a core theoretical pillar;
- Cluster 2 (green): Machine Learning and AI in Financial Prediction. Dominated by works from [11,51,52,53], this cluster signifies the integration of advanced ML and AI techniques into financial prediction. The presence of core ML algorithms like RF and GB highlights a methodological shift towards more sophisticated, data-driven approaches for forecasting financial outcomes. This cluster’s growth, particularly with more recent publications, suggests an increasing emphasis on algorithmic performance and predictive accuracy in CRA;
- Cluster 3 (blue): Knowledge-Based Systems and Expert Applications. Key references within this cluster include [54,55,56]. This cluster reflects research leveraging knowledge-based systems and expert systems for decision support in financial contexts. It often involves the application of AI techniques to structured financial data, aiming to provide actionable insights for risk assessment and corporate analysis. The overlap with machine learning aspects of the second cluster suggests a pragmatic application of predictive models within decision support frameworks;
- Cluster 4 (yellow): Contemporary Expert Systems and Financial Applications. This cluster, featuring [8,9,36,57], signifies more recent developments in the application of expert systems to financial problems. While still focused on expert systems, these references often incorporate newer methodologies or address contemporary financial challenges. Their connections to other clusters suggest a continued evolution and diversification of expert system applications, often integrating with statistical or machine learning paradigms;
- Cluster 5 (purple): Hybrid Models and Decision Support Systems. Including works from [25,58,59,60], this cluster likely represents research focused on hybrid modeling approaches and comprehensive decision support systems. These often combine elements from traditional statistical methods, machine learning, and expert systems to create robust predictive and analytical tools. The interdisciplinary nature of this cluster highlights a trend towards multi-methodological integration to tackle the complexities of corporate financial analysis effectively.
Furthermore, as illustrated in Figure 11, the co-citation density map [45] is highlighted as the most central intellectual node, with enduring influence over decades. In contrast, the second cluster features recent high-impact papers on AI, with more dispersed and evolving citation patterns, suggesting intellectual expansion toward interdisciplinary techniques.
This analysis affirms the field’s dual structure: it is anchored in classic financial modeling while rapidly absorbing innovations from AI, optimization, and data science.
4. Discussion—RQ5 and RQ6
Our analysis demonstrates that CRA in banking and finance has evolved into a strong multidisciplinary field, effectively bridging computer science, finance, and decision sciences. This evolution is evident across several study dimensions, from keyword co-occurrence patterns to the intellectual clusters identified through co-citation analysis.
The interpretation of the keyword co-occurrence of the author keyword (DE) network in Figure 3 provides significant evidence of interdisciplinarity. We believe that the prominence of ML (296 occurrences), AI (157), and DL (73), together with financial terms like bankruptcy prediction (52) and credit risk (41), illustrates a core integration of computational techniques within financial risk domains in general. As we previously noted in 3.7.2, we deliberately distinguish between financial distress and bankruptcy to capture the different risk levels, qualities, and modeling approaches relevant to CRA research, especially given the regulatory and systemic implications in the banking sector. According to our view, the first cluster is focused on ML and hybrid models for bankruptcy and credit default prediction. There, we can find words such as ensemble, DT, and SVM, which highlight the direct application of advanced computational algorithms to solve complex financial problems. The concentration on AI integration in financial services, with keywords like FinTech, digital banking, and blockchain, as documented in the second cluster, further underscores the fusion of computer science innovations with traditional banking operations, which can also be illustrated by the temporal evolution as Figure 4 is showing further accentuation of this trend, and a shift towards more recent concepts like XGBoost and explainability, which are inherently computational but are increasingly applied to enhance transparency and robustness in financial decision-making.
Furthermore, as we notice through our analysis, the keyword co-occurrence of Keywords Plus (ID) network in Figure 5 corroborates and expands upon these findings. Keywords Plus terms such as bankruptcy (227 occurrences), models (218), and classification (146) are central to finance and computational methods. Terms like bankruptcy, models, and classification, found in the first cluster, directly map the computational core of all CRA methods, including default prediction and neural networks.
The second cluster, which focuses on banking, impact, and management, emphasizes the integration of IT acceptance models within the financial services sector. The emergence of topics like blockchain, big data, and internet banking, shown in Figure 6 as recent areas of focus, further solidifies the interdisciplinary bridge, indicating that financial challenges are increasingly being addressed with sophisticated data science and distributed ledger technologies.
Moving forward to our analysis, we study the findings of the bibliographic coupling (documents) in Figure 7, which further illustrates how distinct thematic streams converge through shared foundational literature, reinforcing the multidisciplinary nature of CRA. What we found is that the first cluster, briefly described as bankruptcy prediction and hybrid methodologies, demonstrates how research in financial forecasting leverages advanced computational techniques like ANNs and DTs [24].
Furthermore, it also highlights the application of DL models to textual disclosures for bankruptcy prediction, which is a clear interdisciplinary field combining NLP with financial analysis [23]. In the subfield Advanced Machine Learning for Credit Scoring and Financial Distress, represented by the second cluster, there are included keywords that showcase the intensive use of ensemble methods, ANNs, RF, and SVM in credit scoring and financial distress prediction [27,28].
Cluster 3, AI and FinTech in Finance, explicitly highlights the blurring lines between computer science and traditional finance, creating new avenues for research in areas such as digital servitization [31] and the redefinition of financial relationships through AI [30]. In other words, it directly addresses the transformative impact of AI on financial services, including digital transformation and credit risk assessment [30,31,32].
Finally, our study also examined the co-citation (cited references) analysis as seen in Figure 10 and provided crucial insights into the intellectual foundations. While the first cluster described as Foundational Financial Distress Prediction Models is rooted in classical financial models like [45,48], we notice that the second cluster entitled ML and AI in Financial Prediction distinctly showcases the integration of advanced ML and AI techniques, such as those found in [11,51,53]. This field not only applies new technologies but also builds upon established financial theories, as this can be interpreted by the persistent influence of seminal financial works alongside the increasing co-citation of machine learning method papers. To further exemplify the interdisciplinary nature, combining elements from traditional statistics, ML, and expert systems to create robust analytical tools and the hybrid modeling approaches, we noticed the works of [25,54,55,59,60] in the third cluster and the fifth cluster, respectively.
In summary, according to our bibliometric analysis, we found that CRA has integrated tools from computer science at a fundamental level, creating a new interdisciplinary domain. The goal of this integration is to allow for and further research for more sophisticated risk assessment, prediction, and management, leading to enhanced financial stability and decision-making capabilities.
Before moving on to our final research question regarding the future directions of research, it is essential to acknowledge that, while the present study aims to provide a comprehensive mapping of CRA research by including all publication sources, future analyses could be conducted that focus exclusively on high-ranking journals. Such an approach would offer additional insights into the quality dimension of CRA research outputs, complementing the broader perspective adopted here. Also, it is essential to point out that while this study used Biblioshiny and VOSviewer, we acknowledge that tools like CiteSpace, which could not be used due to the advanced features licensing limitations for big datasets, offer burst analyses to detect emerging topics. Future research could incorporate such methods for deeper trend insights.
Based on the observed trends and intellectual structures, we are moving on to our final research question regarding the future directions of academic research in the field of CRA in banking and finance, and we can identify several promising future research avenues that can be explored to further enrich our understanding of the field. As we mentioned earlier, our research highlighted that the evolving interplay between computational advancements and the complex dynamics of the financial sector can be seen as a significant driving force towards research progress in the field.
Our first suggestion has to do with the inherent black-box nature of many deep learning and ensemble methods that pose significant challenges for regulatory compliance, ethical considerations, and practical adoption in banking. Our study highlights a shift towards more complex AI models. On the other hand, as demonstrated in Figure 4, with the term “explainability”, future research should focus on developing and implementing Explainable Artificial Intelligence (XAI) techniques tailored to financial risk models.
Our second suggestion has to do with the integration of CRA with Environmental, Social, and Governance (ESG) factors that present a burgeoning research frontier. As we demonstrated in Figure 6 regarding the temporal evolution of Keywords Plus, increased attention to “sustainability” and ESG terms has been noticed in the last few years, especially post-2022. Future research could focus on enabling banks to evaluate and manage their portfolios better and to align them with ESG objectives and explore how AI and machine learning models can effectively analyze vast amounts of unstructured ESG data, like corporate reports, news articles, social media, and how to identify and quantify ESG-related financial risks.
Continuing to our third suggestion, through our research, we noticed that advanced data analytics for unstructured and alternative data sources are crucial. While the analysis indicates a move towards textual data in bankruptcy prediction (Mai et al., 2019 [24]), we believe that there is significant potential in leveraging other unstructured and alternative data forms, and taking that into account, research should investigate novel deep learning architectures like transformers for text, graph NN for network data, and advanced feature engineering techniques to extract meaningful insights from these diverse data sources.
Another area of interest for future study, we believe, is that of Decentralized Finance (DeFi) and the increasing prominence of “blockchain”. Figure 4 and Figure 6 signify the growing academic interest in this area. As DeFi ecosystems expand, new forms of financial risks emerge, including smart contract vulnerabilities, liquidity risks in decentralized exchanges, and governance risks in Decentralized Autonomous Organizations (DAOs). Future research could try to explore how CRA methodologies can be adapted to assess and manage these unique risks transparently and securely by developing on-chain risk analytics, applying machine learning to detect anomalies in blockchain transactions, and creating predictive models for DeFi protocol failures, thus understanding the interplay between traditional banking and the evolving DeFi landscape, which, in our view, will be critical for future financial stability.
Another emerging intersection, relatively critical for future financial stability, is the one between Regulatory Technology (RegTech) and CRA. Almost twenty years after the last global financial crisis of 2007, financial regulations have become increasingly complex, and there is a growing need for technology-driven solutions to ensure compliance and enhance supervisory oversight. Taking that into account, we believe that future research could focus on how CRA techniques, particularly AI and ML, can be applied to automate regulatory reporting, detect compliance breaches, and enhance supervisory analytics by developing models for real-time risk monitoring, stress testing, and anti-money laundering (AML) efforts that are both efficient and auditable.
Finally, the dynamic interaction and collaboration of human intelligence and AI in the context of decision support systems in CRA require further investigation. In our view, although AI models can provide sophisticated predictions, the ultimate and final responsibility for risk management should lie with human decision-makers, as they can combine the different kinds of human intelligence with a human sense of art. Optimizing the interaction between human experts, decision makers, and AI systems in CRA with further exploration into designing effective decision support systems that integrate AI insights with human intuition; studying the impact of AI on human cognitive biases in risk assessment; and developing training programs for financial professionals to utilize CRA tools effectively are also avenues of future research exploration.
Author Contributions
Conceptualization, S.J.T. and K.T.; methodology, S.J.T. and K.T.; software, S.J.T.; validation, S.J.T. and K.T.; formal analysis, S.J.T.; resources, S.J.T. and I.C.; data curation, S.J.T.; writing—original draft preparation, S.J.T.; writing—review and editing, S.J.T., I.C. and K.T.; visualization, S.J.T.; supervision, K.T. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The data that support the findings of this study were obtained from the Web of Science Core Collection, a subscription-based database. Access to Web of Science is subject to institutional or personal subscription. The search query and parameters used to retrieve the dataset are detailed in Section 2.1.2 and Table 2 of this article, allowing for reproducibility by users with the necessary access rights.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| AI | Artificial Intelligence |
| ANN | Artificial Neural Network |
| AML | Anti-Money Laundering |
| CI | Collaboration Index/Computational Intelligence (context-dependent) |
| CRA | Computational Risk Analytics |
| CSR | Corporate Social Responsibility |
| DAOs | Decentralized Autonomous Organizations |
| DE | Author Keywords (from bibliometric datasets) |
| DeFi | Decentralized Finance |
| DL | Deep Learning |
| DT | Decision Tree |
| ESG | Environmental, Social, and Governance |
| GB | Gradient Boosting |
| ID | Keywords Plus (from bibliometric datasets) |
| IoT | Internet of Things |
| ML | Machine Learning |
| MCP | Multiple-Country Publications |
| MCP_Ratio | Ratio of Multiple-Country Publications |
| NP | Number of Publications |
| NAY | Number of Active Years |
| PAY | Productivity per Active Year |
| PY | Publication Year/Start Year |
| RF | Random Forest |
| RQ | Research Question |
| SA | Single-authored Publications |
| SCP | Single-Country Publications |
| SVM | Support Vector Machine |
| TC | Total Citations |
| TC/TP | Citations per Publication |
| TLS | Total Link Strength |
| TP | Total Publications |
| VOSviewer | Visualization of Similarities Viewer |
| WoS | Web of Science |
| XAI | Explainable Artificial Intelligence |
| XGBoost | eXtreme Gradient Boosting |
Appendix A
Table A1.
List of journals included in the CRA dataset sorted by the relevant number of articles.
| Journal Title | Number of Articles by Journal |
|---|---|
| APPLIED SOFT COMPUTING | 25 |
| COMPUTATIONAL ECONOMICS | 24 |
| EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 24 |
| RISKS | 23 |
| ANNALS OF OPERATIONS RESEARCH | 21 |
| INTERNATIONAL JOURNAL OF BANK MARKETING | 21 |
| JOURNAL OF FORECASTING | 21 |
| KNOWLEDGE-BASED SYSTEMS | 19 |
| INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS | 18 |
| COGENT ECONOMICS & FINANCE | 15 |
| DECISION SUPPORT SYSTEMS | 15 |
| INFORMATION SCIENCES | 14 |
| INTERNATIONAL JOURNAL OF FINANCIAL STUDIES | 14 |
| JOURNAL OF RISK AND FINANCIAL MANAGEMENT | 14 |
| MATHEMATICAL PROBLEMS IN ENGINEERING | 14 |
| NEURAL COMPUTING & APPLICATIONS | 13 |
| FINANCE RESEARCH LETTERS | 12 |
| HELIYON | 11 |
| APPLIED ECONOMICS LETTERS | 10 |
| FRONTIERS IN ARTIFICIAL INTELLIGENCE | 10 |
| JOURNAL OF FINANCIAL STABILITY | 10 |
| JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | 10 |
| RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE | 10 |
| PLOS ONE | 9 |
| ARTIFICIAL INTELLIGENCE REVIEW | 8 |
| BORSA ISTANBUL REVIEW | 8 |
| COGENT BUSINESS & MANAGEMENT | 8 |
| INTELLIGENT SYSTEMS WITH APPLICATIONS | 8 |
| JOURNAL OF RISK MODEL VALIDATION | 8 |
| NEUROCOMPUTING | 8 |
| QUANTITATIVE FINANCE | 8 |
| APPLIED INTELLIGENCE | 7 |
| COMPLEXITY | 7 |
| FINANCIAL AND CREDIT ACTIVITY-PROBLEMS OF THEORY AND PRACTICE | 7 |
| MACHINE LEARNING WITH APPLICATIONS | 7 |
| OECONOMIA COPERNICANA | 7 |
| PACIFIC-BASIN FINANCE JOURNAL | 7 |
| STRATEGIC CHANGE-BRIEFINGS IN ENTREPRENEURIAL FINANCE | 7 |
| TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY | 7 |
| ECONOMIC MODELLING | 6 |
| INFORMATION | 6 |
| INZINERINE EKONOMIKA-ENGINEERING ECONOMICS | 6 |
| JOURNAL OF BUSINESS RESEARCH | 6 |
| JOURNAL OF CREDIT RISK | 6 |
| JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 6 |
| JOURNAL OF THE KNOWLEDGE ECONOMY | 6 |
| SOFT COMPUTING | 6 |
| COMPUTATIONAL INTELLIGENCE | 5 |
| ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH | 5 |
| FINANCIAL INNOVATION | 5 |
| FUTURE INTERNET | 5 |
| INTERNATIONAL JOURNAL OF FORECASTING | 5 |
| INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING | 5 |
| JOURNAL OF MODELLING IN MANAGEMENT | 5 |
| MANAGEMENT DECISION | 5 |
| SCIENTIFIC REPORTS | 5 |
| APPLIED ECONOMICS | 4 |
| COMPUTERS & SECURITY | 4 |
| DISCRETE DYNAMICS IN NATURE AND SOCIETY | 4 |
| ECONOMICS AND BUSINESS REVIEW | 4 |
| GLOBAL BUSINESS REVIEW | 4 |
| INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 4 |
| INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS | 4 |
| INTERNATIONAL JOURNAL OF EMERGING MARKETS | 4 |
| INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS | 4 |
| INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING | 4 |
| INTERNATIONAL JOURNAL OF ISLAMIC AND MIDDLE EASTERN FINANCE AND MANAGEMENT | 4 |
| JOURNAL OF FINANCIAL REPORTING AND ACCOUNTING | 4 |
| JOURNAL OF FINANCIAL SERVICES MARKETING | 4 |
| JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY | 4 |
| KNOWLEDGE AND INFORMATION SYSTEMS | 4 |
| PACIFIC BUSINESS REVIEW INTERNATIONAL | 4 |
| SYMMETRY-BASEL | 4 |
| SYSTEMS | 4 |
| ACCOUNTING AND FINANCE | 3 |
| DATA | 3 |
| ECONOMIC ANALYSIS AND POLICY | 3 |
| ENGINEERING LETTERS | 3 |
| EUROPEAN JOURNAL OF INNOVATION MANAGEMENT | 3 |
| FUTURE BUSINESS JOURNAL | 3 |
| INTELLIGENT DATA ANALYSIS | 3 |
| INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS | 3 |
| INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING | 3 |
| INTERNATIONAL REVIEW OF ECONOMICS & FINANCE | 3 |
| JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY | 3 |
| JOURNAL OF BANKING & FINANCE | 3 |
| JOURNAL OF BANKING REGULATION | 3 |
| JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE | 3 |
| JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT | 3 |
| JOURNAL OF CENTRAL BANKING THEORY AND PRACTICE | 3 |
| JOURNAL OF INTERNATIONAL MONEY AND FINANCE | 3 |
| JOURNAL OF RESEARCH IN INTERACTIVE MARKETING | 3 |
| JOURNAL OF RETAILING AND CONSUMER SERVICES | 3 |
| LATIN AMERICAN JOURNAL OF CENTRAL BANKING | 3 |
| MONTENEGRIN JOURNAL OF ECONOMICS | 3 |
| NEURAL NETWORK WORLD | 3 |
| NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE | 3 |
| OXFORD REVIEW OF ECONOMIC POLICY | 3 |
| POLISH JOURNAL OF MANAGEMENT STUDIES | 3 |
| PROGRESS IN ARTIFICIAL INTELLIGENCE | 3 |
| QUANTITATIVE FINANCE AND ECONOMICS | 3 |
| REVIEW OF QUANTITATIVE FINANCE AND ACCOUNTING | 3 |
| SYSTEMS AND SOFT COMPUTING | 3 |
| TEHNICKI VJESNIK-TECHNICAL GAZETTE | 3 |
| TRANSFORMATIONS IN BUSINESS & ECONOMICS | 3 |
| ABACUS-A JOURNAL OF ACCOUNTING FINANCE AND BUSINESS STUDIES | 2 |
| ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS | 2 |
| ACTA OECONOMICA | 2 |
| AI | 2 |
| ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2 |
| ARGUMENTA OECONOMICA | 2 |
| ASIA-PACIFIC FINANCIAL MARKETS | 2 |
| BALTIC JOURNAL OF ECONOMIC STUDIES | 2 |
| BENCHMARKING-AN INTERNATIONAL JOURNAL | 2 |
| BUSINESS PROCESS MANAGEMENT JOURNAL | 2 |
| COMPETITIVENESS REVIEW | 2 |
| COMPUTACION Y SISTEMAS | 2 |
| COMPUTERS | 2 |
| CORPORATE GOVERNANCE-THE INTERNATIONAL JOURNAL OF BUSINESS IN SOCIETY | 2 |
| DATA SCIENCE IN FINANCE AND ECONOMICS | 2 |
| E & M EKONOMIE A MANAGEMENT | 2 |
| ELECTRONIC COMMERCE RESEARCH | 2 |
| EMPIRICAL ECONOMICS | 2 |
| EQUILIBRIUM-QUARTERLY JOURNAL OF ECONOMICS AND ECONOMIC POLICY | 2 |
| EUROPEAN FINANCIAL MANAGEMENT | 2 |
| EUROPEAN JOURNAL OF FINANCE | 2 |
| FIIB BUSINESS REVIEW | 2 |
| INGENIERIA SOLIDARIA | 2 |
| INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS | 2 |
| INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT | 2 |
| INTERNATIONAL JOURNAL OF ACCOUNTING AND INFORMATION MANAGEMENT | 2 |
| INTERNATIONAL JOURNAL OF COMPUTATIONAL ECONOMICS AND ECONOMETRICS | 2 |
| INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 2 |
| INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY PROJECT MANAGEMENT | 2 |
| INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2 |
| INTERNATIONAL JOURNAL OF NEURAL SYSTEMS | 2 |
| INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT | 2 |
| INTERNATIONAL JOURNAL OF TECHNOLOGY | 2 |
| INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS | 2 |
| INVESTMENT ANALYSTS JOURNAL | 2 |
| JOURNAL OF ACCOUNTING LITERATURE | 2 |
| JOURNAL OF APPLIED ACCOUNTING RESEARCH | 2 |
| JOURNAL OF ASIAN FINANCE ECONOMICS AND BUSINESS | 2 |
| JOURNAL OF BUSINESS FINANCE & ACCOUNTING | 2 |
| JOURNAL OF ECONOMIC SURVEYS | 2 |
| JOURNAL OF FINANCIAL SERVICES RESEARCH | 2 |
| JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION | 2 |
| JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES | 2 |
| JOURNAL OF MANAGEMENT ANALYTICS | 2 |
| JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING | 2 |
| JOURNAL OF MONETARY ECONOMICS | 2 |
| JOURNAL OF MONEY CREDIT AND BANKING | 2 |
| JOURNAL OF SMALL BUSINESS MANAGEMENT | 2 |
| MARKETING AND MANAGEMENT OF INNOVATIONS | 2 |
| OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE | 2 |
| QUARTERLY REVIEW OF ECONOMICS AND FINANCE | 2 |
| REVIEW OF FINANCIAL ECONOMICS | 2 |
| REVISTA INTERNACIONAL DE METODOS NUMERICOS PARA CALCULO Y DISENO EN INGENIERIA | 2 |
| ROMANIAN JOURNAL OF ECONOMIC FORECASTING | 2 |
| SAGE OPEN | 2 |
| SOCIAL NETWORK ANALYSIS AND MINING | 2 |
| SOCIO-ECONOMIC PLANNING SCIENCES | 2 |
| TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS | 2 |
| ACADEMIA-REVISTA LATINOAMERICANA DE ADMINISTRACION | 1 |
| ACCOUNTING HORIZONS | 1 |
| ACCOUNTING REVIEW | 1 |
| ACM JOURNAL OF DATA AND INFORMATION QUALITY | 1 |
| ACTA INFOLOGICA | 1 |
| ACTA INFORMATICA PRAGENSIA | 1 |
| ADMINISTRATIVE SCIENCES | 1 |
| ADVANCES IN ACCOUNTING | 1 |
| AFRICAN JOURNAL OF BUSINESS MANAGEMENT | 1 |
| AFRICAN JOURNAL OF ECONOMIC AND MANAGEMENT STUDIES | 1 |
| AFRICAN JOURNAL OF SCIENCE TECHNOLOGY INNOVATION & DEVELOPMENT | 1 |
| AIN SHAMS ENGINEERING JOURNAL | 1 |
| ALEXANDRIA ENGINEERING JOURNAL | 1 |
| AMERICAN ECONOMIC REVIEW | 1 |
| ANALES DEL INSTITUTO DE ACTUARIOS ESPANOLES | 1 |
| ANNALS OF FINANCIAL ECONOMICS | 1 |
| APPLIED ECONOMICS JOURNAL | 1 |
| ASIA-PACIFIC JOURNAL OF ACCOUNTING & ECONOMICS | 1 |
| ASIA-PACIFIC JOURNAL OF BUSINESS ADMINISTRATION | 1 |
| ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES | 1 |
| ASR CHIANG MAI UNIVERSITY JOURNAL OF SOCIAL SCIENCES AND HUMANITIES | 1 |
| AUSTRALASIAN ACCOUNTING BUSINESS AND FINANCE JOURNAL | 1 |
| BANKS AND BANK SYSTEMS | 1 |
| BIG DATA & SOCIETY | 1 |
| BRAZILIAN JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT | 1 |
| BRITISH ACCOUNTING REVIEW | 1 |
| BRITISH JOURNAL OF MANAGEMENT | 1 |
| BULLETIN OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN | 1 |
| CENTRAL BANK REVIEW | 1 |
| CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH | 1 |
| CENTRAL EUROPEAN MANAGEMENT JOURNAL | 1 |
| CHINA ECONOMIC REVIEW | 1 |
| CIENCIA ERGO-SUM | 1 |
| CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 1 |
| COGENT SOCIAL SCIENCES | 1 |
| COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS | 1 |
| COMPLEX & INTELLIGENT SYSTEMS | 1 |
| COMPUTATION | 1 |
| COMPUTATIONAL MANAGEMENT SCIENCE | 1 |
| COMPUTING AND INFORMATICS | 1 |
| CONSTRUCTION MANAGEMENT AND ECONOMICS | 1 |
| CROATIAN ECONOMIC SURVEY | 1 |
| DATA & KNOWLEDGE ENGINEERING | 1 |
| DECISION SCIENCE LETTERS | 1 |
| DISCOVER COMPUTING | 1 |
| ECONOMETRICS | 1 |
| ECONOMIC INQUIRY | 1 |
| ECONOMIC SYSTEMS | 1 |
| ECONOMICS & SOCIOLOGY | 1 |
| ECONOMICS AND FINANCE LETTERS | 1 |
| ECONOMICS OF TRANSITION AND INSTITUTIONAL CHANGE | 1 |
| ECONOMICS-THE OPEN ACCESS OPEN-ASSESSMENT E-JOURNAL | 1 |
| EGE ACADEMIC REVIEW | 1 |
| EKONOMI POLITIKA & FINANS ARASTIRMALARI DERGISI | 1 |
| EKONOMIA I PRAWO-ECONOMICS AND LAW | 1 |
| ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS | 1 |
| EMERGING MARKETS REVIEW | 1 |
| ENGINEERING RESEARCH EXPRESS | 1 |
| ENTERPRISE INFORMATION SYSTEMS | 1 |
| ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES | 1 |
| EPJ DATA SCIENCE | 1 |
| EURASIAN BUSINESS REVIEW | 1 |
| EUROMED JOURNAL OF BUSINESS | 1 |
| EUROPEAN ACCOUNTING REVIEW | 1 |
| EUROPEAN BUSINESS REVIEW | 1 |
| EUROPEAN MANAGEMENT STUDIES | 1 |
| EUROPEAN RESEARCH ON MANAGEMENT AND BUSINESS ECONOMICS | 1 |
| EVOLUTIONARY INTELLIGENCE | 1 |
| FINANCE A UVER-CZECH JOURNAL OF ECONOMICS AND FINANCE | 1 |
| FORECASTING | 1 |
| FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS | 1 |
| FRONTIERS OF BUSINESS RESEARCH IN CHINA | 1 |
| FUDAN JOURNAL OF THE HUMANITIES AND SOCIAL SCIENCES | 1 |
| GENEVA PAPERS ON RISK AND INSURANCE-ISSUES AND PRACTICE | 1 |
| GREY SYSTEMS-THEORY AND APPLICATION | 1 |
| HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES | 1 |
| IIMB MANAGEMENT REVIEW | 1 |
| IMA JOURNAL OF MANAGEMENT MATHEMATICS | 1 |
| INDUSTRIAL AND CORPORATE CHANGE | 1 |
| INFOR | 1 |
| INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL | 1 |
| INGENIERIA | 1 |
| INGENIERIA E INVESTIGACION | 1 |
| INNOVATIVE MARKETING | 1 |
| INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES | 1 |
| INTERNATIONAL JOURNAL OF ASIAN BUSINESS AND INFORMATION MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 1 |
| INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS | 1 |
| INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS | 1 |
| INTERNATIONAL JOURNAL OF DISCLOSURE AND GOVERNANCE | 1 |
| INTERNATIONAL JOURNAL OF E-COLLABORATION | 1 |
| INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS | 1 |
| INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF ENTERPRISE INFORMATION SYSTEMS | 1 |
| INTERNATIONAL JOURNAL OF ENTREPRENEURSHIP & SMALL BUSINESS | 1 |
| INTERNATIONAL JOURNAL OF ETHICS AND SYSTEMS | 1 |
| INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH | 1 |
| INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING | 1 |
| INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 1 |
| INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 1 |
| INTERNATIONAL JOURNAL OF ORGANIZATIONAL ANALYSIS | 1 |
| INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF PRODUCTIVITY AND PERFORMANCE MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS | 1 |
| INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT | 1 |
| INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS | 1 |
| IRANIAN JOURNAL OF MANAGEMENT STUDIES | 1 |
| ISTANBUL BUSINESS RESEARCH | 1 |
| JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 1 |
| JOURNAL OF APPLIED ECONOMICS | 1 |
| JOURNAL OF ASIA BUSINESS STUDIES | 1 |
| JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES | 1 |
| JOURNAL OF BEHAVIORAL FINANCE | 1 |
| JOURNAL OF BUSINESS & INDUSTRIAL MARKETING | 1 |
| JOURNAL OF CASES ON INFORMATION TECHNOLOGY | 1 |
| JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS | 1 |
| JOURNAL OF COMPUTER INFORMATION SYSTEMS | 1 |
| JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES | 1 |
| JOURNAL OF CONSUMER BEHAVIOUR | 1 |
| JOURNAL OF CONTEMPORARY ACCOUNTING & ECONOMICS | 1 |
| JOURNAL OF CORPORATE ACCOUNTING AND FINANCE | 1 |
| JOURNAL OF CORPORATE FINANCE | 1 |
| JOURNAL OF DEVELOPMENT ECONOMICS | 1 |
| JOURNAL OF ECONOMETRICS | 1 |
| JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION | 1 |
| JOURNAL OF ECONOMIC DYNAMICS & CONTROL | 1 |
| JOURNAL OF ECONOMIC POLICY RESEARCHES-IKTISAT POLITIKASI ARASTIRMALARI DERGISI | 1 |
| JOURNAL OF ECONOMICS AND FINANCE | 1 |
| JOURNAL OF ECONOMICS, FINANCE AND ADMINISTRATIVE SCIENCE | 1 |
| JOURNAL OF EMERGING MARKET FINANCE | 1 |
| JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY | 1 |
| JOURNAL OF FINANCE AND DATA SCIENCE | 1 |
| JOURNAL OF FINANCIAL ECONOMIC POLICY | 1 |
| JOURNAL OF FINANCIAL ECONOMICS | 1 |
| JOURNAL OF FINANCIAL INTERMEDIATION | 1 |
| JOURNAL OF FINANCIAL REPORTING | 1 |
| JOURNAL OF GLOBAL RESPONSIBILITY | 1 |
| JOURNAL OF INDUSTRIAL AND BUSINESS ECONOMICS | 1 |
| JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA | 1 |
| JOURNAL OF INFORMATION TECHNOLOGY RESEARCH | 1 |
| JOURNAL OF INNOVATION & KNOWLEDGE | 1 |
| JOURNAL OF INTELLIGENT INFORMATION SYSTEMS | 1 |
| JOURNAL OF INTERNATIONAL COMMERCE ECONOMICS AND POLICY | 1 |
| JOURNAL OF INTERNATIONAL ECONOMICS | 1 |
| JOURNAL OF INTERNATIONAL FINANCIAL MANAGEMENT & ACCOUNTING | 1 |
| JOURNAL OF ISLAMIC ACCOUNTING AND BUSINESS RESEARCH | 1 |
| JOURNAL OF MANAGEMENT & ORGANIZATION | 1 |
| JOURNAL OF MANAGEMENT AND GOVERNANCE | 1 |
| JOURNAL OF MARKETING ANALYTICS | 1 |
| JOURNAL OF MATHEMATICS IN INDUSTRY | 1 |
| JOURNAL OF OPERATIONAL RISK | 1 |
| JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE | 1 |
| JOURNAL OF QUANTITATIVE ECONOMICS | 1 |
| JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH | 1 |
| JOURNAL OF SERVICE MANAGEMENT | 1 |
| JOURNAL OF STRATEGIC MARKETING | 1 |
| JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY | 1 |
| JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN | 1 |
| JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH | 1 |
| KNOWLEDGE ENGINEERING REVIEW | 1 |
| KUWAIT JOURNAL OF SCIENCE | 1 |
| MACHINE LEARNING | 1 |
| MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES | 1 |
| MANAGEMENT RESEARCH AND PRACTICE | 1 |
| MANAGEMENT REVIEW QUARTERLY | 1 |
| MANAGEMENT SCIENCE | 1 |
| MANAGEMENT-POLAND | 1 |
| MANAGERIAL FINANCE | 1 |
| MARKET-TRZISTE | 1 |
| MEASUREMENT-INTERDISCIPLINARY RESEARCH AND PERSPECTIVES | 1 |
| METHODSX | 1 |
| NANKAI BUSINESS REVIEW INTERNATIONAL | 1 |
| NATIONAL ACCOUNTING REVIEW | 1 |
| NATURE COMMUNICATIONS | 1 |
| NATURE MACHINE INTELLIGENCE | 1 |
| NAVAL RESEARCH LOGISTICS | 1 |
| NETWORKS AND HETEROGENEOUS MEDIA | 1 |
| NEW MATHEMATICS AND NATURAL COMPUTATION | 1 |
| OPEN ECONOMIES REVIEW | 1 |
| OPERATIONS RESEARCH AND DECISIONS | 1 |
| OR SPECTRUM | 1 |
| PANOECONOMICUS | 1 |
| PATTERN RECOGNITION AND IMAGE ANALYSIS | 1 |
| PATTERN RECOGNITION LETTERS | 1 |
| PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES | 1 |
| PUBLIC FINANCE QUARTERLY-HUNGARY | 1 |
| QUALITATIVE RESEARCH IN FINANCIAL MARKETS | 1 |
| RAE-REVISTA DE ADMINISTRACAO DE EMPRESAS | 1 |
| REAL ESTATE MANAGEMENT AND VALUATION | 1 |
| RETOS-REVISTA DE CIENCIAS DE LA ADMINISTRACION Y ECONOMIA | 1 |
| REVIEW OF ACCOUNTING AND FINANCE | 1 |
| REVIEW OF ACCOUNTING STUDIES | 1 |
| REVIEW OF DEVELOPMENT FINANCE | 1 |
| REVIEW OF FINANCE | 1 |
| REVISTA GESTAO & TECNOLOGIA-JOURNAL OF MANAGEMENT AND TECHNOLOGY | 1 |
| REVISTA INNOVACIENCIA | 1 |
| REVISTA UNIVERSIDAD EMPRESA | 1 |
| RISK MANAGEMENT-AN INTERNATIONAL JOURNAL | 1 |
| RISUS-JOURNAL ON INNOVATION AND SUSTAINABILITY | 1 |
| SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES | 1 |
| SCANDINAVIAN JOURNAL OF ECONOMICS | 1 |
| SCIENCE TECHNOLOGY AND SOCIETY | 1 |
| SCIENTIFIC BULLETIN OF MUKACHEVO STATE UNIVERSITY-SERIES ECONOMICS | 1 |
| SCIENTIFIC DATA | 1 |
| SERBIAN JOURNAL OF MANAGEMENT | 1 |
| SERVICE BUSINESS | 1 |
| SERVICE INDUSTRIES JOURNAL | 1 |
| SOUTH AFRICAN JOURNAL OF BUSINESS MANAGEMENT | 1 |
| SOUTH AFRICAN JOURNAL OF ECONOMIC AND MANAGEMENT SCIENCES | 1 |
| SPANISH JOURNAL OF FINANCE AND ACCOUNTING-REVISTA ESPANOLA DE FINANCIACION Y CONTABILIDAD | 1 |
| SPATIAL ECONOMIC ANALYSIS | 1 |
| SRI LANKA JOURNAL OF SOCIAL SCIENCES | 1 |
| STATISTIKA-STATISTICS AND ECONOMY JOURNAL | 1 |
| TECHNICS TECHNOLOGIES EDUCATION MANAGEMENT-TTEM | 1 |
| TECHNOLOGIES | 1 |
| TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT | 1 |
| TURKISH JOURNAL OF ISLAMIC ECONOMICS-TUJISE | 1 |
| WEB INTELLIGENCE | 1 |
| ZBORNIK RADOVA EKONOMSKOG FAKULTETA U RIJECI-PROCEEDINGS OF RIJEKA FACULTY OF ECONOMICS | 1 |
References
- Fethi, M.D.; Pasiouras, F. Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A Survey. Eur. J. Oper. Res. 2010, 204, 189–198. [Google Scholar] [CrossRef]
- Khandani, A.E.; Kim, A.J.; Lo, A.W. Consumer Credit-Risk Models via Machine-Learning Algorithms. J. Bank. Financ. 2010, 34, 2767–2787. [Google Scholar] [CrossRef]
- Bahrammirzaee, A. A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Comput. Appl. 2010, 19, 1165–1195. [Google Scholar] [CrossRef]
- Veganzones, D.; Severin, E. Corporate Failure Prediction Models in the Twenty-First Century: A Review. Eur. Bus. Rev. 2021, 33, 291–316. [Google Scholar] [CrossRef]
- Ngai, E.W.T.; Chau, D.C.K.; Chan, T.L.A. The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decis. Support Syst. 2011, 50, 775–788. [Google Scholar] [CrossRef]
- Tinoco, M.H.; Wilson, N. Financial Distress and Bankruptcy Prediction among Listed Companies Using Accounting, Market and Macroeconomic Variables. Int. Rev. Financ. Anal. 2013, 30, 394–419. [Google Scholar] [CrossRef]
- Geng, R.; Bose, I.; Chen, X. Prediction of Financial Distress: An Empirical Study of Listed Chinese Companies Using Data Mining. Eur. J. Oper. Res. 2015, 241, 236–247. [Google Scholar] [CrossRef]
- Sun, J.; Luo, Y.; Chen, Y. Predicting Financial Distress and Corporate Failure: A Review from the State-of-the-Art Definitions, Modeling, Sampling, and Featuring Approaches. Knowl. Based Syst. 2014, 57, 41–56. [Google Scholar] [CrossRef]
- Alaka, H.A.; Oyedele, L.O.; Owolabi, H.A.; Kumar, V.; Ajayi, S.O.; Akinade, O.O.; Bilal, M.; Han, S. Systematic Review of Bankruptcy Prediction Models: Towards a Framework for Tool Selection. Expert Syst. Appl. 2018, 94, 164–184. [Google Scholar] [CrossRef]
- Goodell, J.W.; Kumar, S.; Lim, W.M.; Pattnaik, D. Artificial Intelligence and Machine Learning in Finance: Identifying Foundations, Themes, and Research Clusters from Bibliometric Analysis. J. Behav. Exp. Financ. 2021, 32, 100577. [Google Scholar] [CrossRef]
- Barboza, F.; Kimura, H.; Altman, E.I. Machine Learning Models and Bankruptcy Prediction. Expert Syst. Appl. 2017, 83, 405–417. [Google Scholar] [CrossRef]
- Kumar, A.; Sharma, S.; Mahdavi, M. Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review. Risks 2021, 9, 192. [Google Scholar] [CrossRef]
- Andronie, M.; Andronie, I.E.; Dragomir, L.C.; Șerban-Oprescu, G.L. Big Data Management Algorithms in Artificial Internet of Things-Based Fintech. Oeconomia Copernic. 2023, 14, 287–312. [Google Scholar] [CrossRef]
- Lazaroiu, G.; Andronie, M.; Uţă, C.; Hurloiu, I. Artificial Intelligence Algorithms and Cloud Computing Technologies in Blockchain-Based Fintech Management. Oeconomia Copernic. 2023, 14, 89–113. [Google Scholar] [CrossRef]
- Amarnadh, V.; Moparthi, N.R. Comprehensive Review of Different Artificial Intelligence-Based Methods for Credit Risk Assessment in Data Science. Intell. Decis. Technol. 2023, 17, 227–241. [Google Scholar] [CrossRef]
- Van Eck, N.J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef]
- Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
- Pranckutė, R. Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications 2021, 9, 12. [Google Scholar] [CrossRef]
- Pritchard, A. Statistical Bibliography or Bibliometrics. J. Doc. 1969, 25, 348–349. [Google Scholar]
- Linnenluecke, M.K.; Marrone, M.; Singh, A.K. Conducting Systematic Literature Reviews and Bibliometric Analyses. Aust. J. Manag. 2020, 45, 175–194. [Google Scholar] [CrossRef]
- Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to Conduct a Bibliometric Analysis: An Overview and Guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
- Moro, S.; Cortez, P.; Rita, P. Business Intelligence in Banking: A Literature Analysis from 2002 to 2013 Using Text Mining and Latent Dirichlet Allocation. Expert Syst. Appl. 2015, 42, 1314–1324. [Google Scholar] [CrossRef]
- Cheng, M.; Qu, Y. Does Bank FinTech Reduce Credit Risk? Evidence from China. Pac. Basin Financ. J. 2020, 63, 101398. [Google Scholar] [CrossRef]
- Mai, F.; Tian, S.; Lee, C.; Ma, L. Deep Learning Models for Bankruptcy Prediction Using Textual Disclosures. Eur. J. Oper. Res. 2019, 274, 743–758. [Google Scholar] [CrossRef]
- Alfaro, E.; García, N.; Gámez, M.; Elizondo, D. Bankruptcy Forecasting: An Empirical Comparison of AdaBoost and Neural Networks. Decis. Support Syst. 2008, 45, 110–122. [Google Scholar] [CrossRef]
- Chou, C.; Hsieh, S.; Qiu, C. Hybrid Genetic Algorithm and Fuzzy Clustering for Bankruptcy Prediction. Appl. Soft Comput. 2017, 56, 298–316. [Google Scholar] [CrossRef]
- Tkáč, M.; Verner, R. Artificial Neural Networks in Business: Two Decades of Research. Appl. Soft Comput. 2015, 38, 788–804. [Google Scholar] [CrossRef]
- Akkoç, S. An Empirical Comparison of Conventional Techniques, Neural Networks and the Three Stage Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Model for Credit Scoring Analysis: The Case of Turkish Credit Card Data. Eur. J. Oper. Res. 2012, 222, 168–178. [Google Scholar] [CrossRef]
- Chen, N.; Ribeiro, B.; Chen, A. Financial Credit Risk Assessment: A Recent Review. Artif. Intell. Rev. 2016, 45, 1–23. [Google Scholar] [CrossRef]
- Flavián, C.; Ibáñez-Sánchez, S.; Orús, C. The Impact of Virtual, Augmented and Mixed Reality Technologies on the Customer Experience. J. Bus. Res. 2022, 142, 620–632. [Google Scholar] [CrossRef]
- Payne, E.H.M.; Dahl, A.J.; Peltier, J. Digital Servitization Value Co-Creation Framework for AI Services: A Research Agenda for Digital Transformation in Financial Service Ecosystems. J. Res. Interact. Mark. 2021, 15, 200–222. [Google Scholar] [CrossRef]
- Hentzen, J.; Hoffmann, A.; Dolan, R.; Pala, E. Artificial Intelligence in Customer-Facing Financial Services: A Systematic Literature Review and Agenda for Future Research. Int. J. Bank Mark. 2021, 40, 1299–1336. [Google Scholar] [CrossRef]
- Milana, C.; Ashta, A. Artificial Intelligence Techniques in Finance and Financial Markets: A Survey of the Literature. Strateg. Change 2021, 30, 189–209. [Google Scholar] [CrossRef]
- Kou, G.; Chao, X.; Peng, Y.; Alsaadi, F.E.; Herrera-Viedma, E. Machine Learning Methods for Systemic Risk Analysis in Financial Sectors. Technol. Econ. Dev. Econ. 2019, 25, 716–742. [Google Scholar] [CrossRef]
- Amani, F.A.; Fadlalla, A.M. Data Mining Applications in Accounting: A Review of the Literature and Organizing Framework. Int. J. Account. Inf. Syst. 2017, 24, 32–58. [Google Scholar] [CrossRef]
- Liang, D.; Lu, C.; Tsai, C.; Shih, G. Financial Ratios and Corporate Governance Indicators in Bankruptcy Prediction: A Comprehensive Study. Eur. J. Oper. Res. 2016, 252, 561–572. [Google Scholar] [CrossRef]
- Liang, D.; Tsai, C.; Lu, H.; Chang, L. Combining Corporate Governance Indicators with Stacking Ensembles for Financial Distress Prediction. J. Bus. Res. 2020, 120, 137–146. [Google Scholar] [CrossRef]
- Ben Jabeur, S.; Stef, N.; Carmona, P. Bankruptcy Prediction Using the XGBoost Algorithm and Variable Importance Feature Engineering. Comput. Econ. 2023, 61, 715–741. [Google Scholar] [CrossRef]
- Demyanyk, Y.; Hasan, I. Financial Crises and Bank Failures: A Review of Prediction Methods. Omega 2010, 38, 315–324. [Google Scholar] [CrossRef]
- Butaru, F.; Chen, Q.; Clark, B.; Das, S.; Lo, A.W.; Siddique, A. Risk and Risk Management in the Credit Card Industry. J. Bank. Financ. 2016, 72, 218–239. [Google Scholar] [CrossRef]
- Chen, Y.; Cheng, C. Hybrid Models Based on Rough Set Classifiers for Setting Credit Rating Decision Rules in the Global Banking Industry. Knowl. Based Syst. 2013, 39, 224–239. [Google Scholar] [CrossRef]
- Petropoulos, A.; Siakoulis, V.; Stavroulakis, E.; Vlachogiannakis, N.E. Predicting Bank Insolvencies Using Machine Learning Techniques. Int. J. Forecast. 2020, 36, 1092–1113. [Google Scholar] [CrossRef]
- Climent, F.; Momparler, A.; Carmona, P. Anticipating Bank Distress in the Eurozone: An Extreme Gradient Boosting Approach. J. Bus. Res. 2019, 101, 885–896. [Google Scholar] [CrossRef]
- Jakšič, M.; Marinč, M. Relationship Banking and Information Technology: The Role of Artificial Intelligence and FinTech. Risk Manag. 2019, 21, 1–18. [Google Scholar] [CrossRef]
- Altman, E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. J. Financ. 1968, 23, 589. [Google Scholar] [CrossRef]
- Altman, E.I.; Marco, G.; Varetto, F. Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience). J. Bank. Financ. 1994, 18, 505–529. [Google Scholar] [CrossRef]
- Beaver, W.H. Financial Ratios As Predictors of Failure. J. Account. Res. 1966, 4, 71–111. [Google Scholar] [CrossRef]
- Ohlson, J.A. Financial Ratios and the Probabilistic Prediction of Bankruptcy. J. Account. Res. 1980, 18, 109–131. [Google Scholar] [CrossRef]
- Martin, D. Early Warning of Bank Failure: A Logit Regression Approach. J. Bank. Financ. 1977, 1, 249–276. [Google Scholar] [CrossRef]
- Zmijewski, M.E. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. J. Account. Res. 1984, 22, 59–82. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar]
- Lessmann, S.; Baesens, B.; Seow, H.; Thomas, L.C. Benchmarking State-of-the-Art Classification Algorithms for Credit Scoring: An Update of Research. Eur. J. Oper. Res. 2015, 247, 124–136. [Google Scholar] [CrossRef]
- Tsai, C.; Wu, J. Using Neural Network Ensembles for Bankruptcy Prediction and Credit Scoring. Expert Syst. Appl. 2008, 34, 2639–2649. [Google Scholar] [CrossRef]
- Tsai, C. Feature Selection in Bankruptcy Prediction. Knowl. Based Syst. 2009, 22, 120–127. [Google Scholar] [CrossRef]
- Liang, D.; Tsai, C.; Wu, H. The Effect of Feature Selection on Financial Distress Prediction. Knowl. Based Syst. 2015, 73, 289–297. [Google Scholar] [CrossRef]
- Zięba, M.; Tomczak, S.; Tomczak, J. Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction. Expert Syst. Appl. 2016, 58, 93–101. [Google Scholar] [CrossRef]
- Kim, M.; Kang, D. Ensemble with Neural Networks for Bankruptcy Prediction. Expert Syst. Appl. 2010, 37, 3373–3379. [Google Scholar] [CrossRef]
- Olson, D.L.; Delen, D.; Meng, Y. Comparative Analysis of Data Mining Methods for Bankruptcy Prediction. Decis. Support Syst. 2011, 52, 464–473. [Google Scholar] [CrossRef]
- Wang, G.; Ma, J.; Yang, S. An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction. Expert Syst. Appl. 2014, 41, 2353–2361. [Google Scholar] [CrossRef]
Figure 1.
Annual scientific production on CRA in banking (2000–2025).
Figure 2.
Co-authorship (author collaboration networks) of CRA in banking.
Figure 3.
Keyword co-occurrence (DE—author keywords) network.
Figure 4.
Temporal evolution of keyword co-occurrence (DE—author keywords) network.
Figure 5.
Keyword co-occurrence (ID—Keywords Plus) network.
Figure 6.
Temporal evolution of keyword co-occurrence (ID—Keywords Plus) network.
Figure 7.
Bibliographic coupling in documents of CRA.
Figure 8.
Temporal evolution of bibliographic coupling in documents of CRA.
Figure 9.
Citation overlay map of bibliographic coupling in documents of CRA.
Figure 10.
Co-citation analysis. Cited references of CRA.
Figure 11.
Density map of cited co-citation references of CRA.
Table 1.
Comparison of previous studies with the present study.
| Author Name (Year) | Time Period | Focus of the Study | Database Used | Number of Documents | Methodology | Contribution | Keywords Used |
|---|---|---|---|---|---|---|---|
| Ngai et al. (2011) [5] | 1997–2010 | Financial fraud detection via DM | WoS, Scopus | 49 articles | Systematic literature review | Classification framework for fraud detection | DM, fraud detection, classification |
| Amarnadh & Moparthi (2023) [15] | 2000–2023 | AI-based credit risk assessment methods | WoS | 85 articles | Comprehensive review | Overview of AI techniques in credit risk assessment | Credit risk, AI, data science |
| Fethi & Pasiouras (2010) [1] | 1998–2008 | Bank efficiency assessment using AI/OR | WoS | 196 articles | Bibliometric review | Survey of AI and OR methods in banking | AI, OR, banking efficiency |
| Veganzones & Severin (2021) [4] | 2000–2020 | Corporate failure prediction models | WoS | 95 articles | Systematic literature review | Updated synthesis of corporate failure models | Bankruptcy, failure prediction, financial distress |
| Bahrammirzaee (2010) [3] | 1994–2009 | AI applications in finance | WoS, Scopus | 125 articles | Comparative review | Review of ANNs, expert systems, and hybrid models | AI, expert systems, ANN |
| Barboza et al. (2017) [11] | 1995–2014 | Bankruptcy prediction with ML | WoS | 130 articles | Meta-review | Comparative performance of ML bankruptcy models | Bankruptcy, ML, prediction |
| Andronie et al. (2023) [13] | 2010–2023 | BDA management algorithms in FinTech | WoS, Scopus | 75 articles | Systematic review | Applications of BDA and IoT in FinTech | BDA, FinTech, AI, IoT |
| Alaka et al. (2018) [9] | 2000–2017 | Corporate default prediction via ML | WoS | 95 articles | Systematic review | Evaluation of ML vs. classical corporate default models | Corporate default, ML, bankruptcy |
| Kumar et al. (2021) [12] | 2000–2020 | Digital credit scoring for rural finance | WoS | 63 articles | Literature review | ML applications in rural microcredit | Credit scoring, rural finance, ML |
| Lazaroiu et al. (2023) [14] | 2010–2023 | Blockchain-based FinTech management | WoS | 45 articles | Systematic review | AI, cloud computing, and blockchain integration | Blockchain, FinTech, AI |
Table 2.
Final query and refinement steps are used for data collection.
| No. | Search Query | Results |
|---|---|---|
| #1 | Final query: TS = ((“computational intelligence” OR “machine learning” OR “artificial intelligence” OR “data mining” OR “deep learning” OR “neural network”) AND (“bank*” OR “financial institution*” OR “credit risk” OR “risk management” OR “systemic risk”) AND financ* AND bank*). | 2692 |
| #2 | Limiting the study to studies published from 2000 to 2025. | 2655 |
| #3 | Included only fields of economics, finance, business, operations, computer science, engineering, and multidisciplinary journals. | 1120 |
| #4 | Limited online to English journals, research articles, and reviews, | 1083 |
Table 3.
Publication and citation metrics for CRA research.
| Statistic | Value |
|---|---|
| Panel A. Publication Metrics | |
| Total publications (TP) | 1083 |
| Article | 1021 |
| Review | 62 |
| Number of active years (NAY) | 26 |
| Productivity per active year (PAY) | 43.32 |
| Panel B. Citation Metrics | |
| Total citations (TC) | 21,556 |
| Average citations per publication (TC/TP) | 19.9 |
| h-index | 72 |
| g-index | 114 |
| Panel C. Co-authorship Metrics | |
| Number of contributing authors (NCA) | 3387 |
| Number of unique authors (NUA) | 2952 |
| Authors of single-authored publications (ASA) | 103 |
| Single-authored publications (SA) | 110 |
| Co-authored publications (CA) | 973 |
| Collaboration index (CI) | 3.13 |
| International co-authorships (%) (ICA) | 30.38% |
Table 4.
List of top contributing journals to CRA research by total citations.
| Source Journal Titles | TC | NP | TC/NP | h- Index | PY_ Start | Scopus SJR Quartile | WoS JCR Quartile |
|---|---|---|---|---|---|---|---|
| Decision Support Systems | 2009 | 15 | 133.93 | 11 | 2008 | Q1 | Q1 |
| European Journal of Operational Research | 1925 | 24 | 80.21 | 18 | 2006 | Q1 | Q1 |
| Applied Soft Computing | 1603 | 25 | 64.12 | 18 | 2008 | Q1 | |
| Knowledge-Based Systems | 1524 | 19 | 80.21 | 18 | 2006 | Q1 | Q1 |
| Neural Computing and Applications | 937 | 13 | 72.08 | 10 | 2010 | Q1 | Q1 |
| International Journal of Bank Marketing | 513 | 21 | 24.43 | 10 | 2015 | Q2 | |
| Information Sciences | 508 | 14 | 36.29 | 10 | 2007 | Q1 | Q1 |
| Journal of Banking and Finance | 476 | 3 | 158.67 | 3 | 2010 | Q1 | Q1 |
| International Review of Financial Analysis | 460 | 18 | 25.56 | 11 | 2013 | Q1 | Q1 |
| Technological & Economic Development of Economy | 450 | 7 | 64.29 | 5 | 2012 | Q2 | Q3 |
| Research in International Business and Finance | 369 | 10 | 36.90 | 7 | 2018 | Q2 | Q3 |
| Journal of Forecasting | 342 | 21 | 16.29 | 11 | 2000 | Q2 | Q2 |
| Journal of Business Research | 325 | 6 | 54.17 | 6 | 2018 | Q1 | Q1 |
| Journal of Research in Interactive Marketing | 321 | 3 | 107.00 | 3 | 2018 | Q2 | Q3 |
| Neurocomputing | 318 | 8 | 39.75 | 7 | 2010 | Q1 | Q1 |
Note: Source journal titles that received a minimum of 300 citations are shown in the list. TC = total citations; NP = number of publications; h = H-Index; TC/NP = citations per publication; PY = publication start year.
Table 5.
Most globally cited articles on CRA in banking.
| Sr. No. | Article Title | 1st Author (Year) | Source Title | Cited by |
|---|---|---|---|---|
| 1 | The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature | Ngai EWT (2011) [5] | Decision Support Systems | 595 |
| 2 | A data-driven approach to predict the success of bank telemarketing | Moro S (2015) [22] | Decision Support Systems | 492 |
| 3 | Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey | Fethi MD (2010) [1] | European Journal of Operational Research | 452 |
| 4 | Consumer credit-risk models via machine-learning algorithms | Khandani AE (2010) [2] | Journal of Banking & Finance | 357 |
| 5 | A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems | Bahrammirzaee A (2010) [3] | Neural Computing & Applications | 301 |
| 6 | Prediction of financial distress: An empirical study of listed Chinese companies using data mining | Geng R (2015) [7] | European Journal of Operational Research | 294 |
| 7 | Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables | Tinoco MH (2013) [6] | International Review of Financial Analysis | 238 |
| 8 | Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches | Sun J. (2014) [8] | Knowledge-Based Systems | 230 |
| 9 | Does bank FinTech reduce credit risk? Evidence from China | Cheng MY (2020) [23] | Pacific-Basin Finance Journal | 224 |
| 10 | Deep learning models for bankruptcy prediction using textual disclosures | Mai F (2019) [24] | European Journal of Operational Research | 212 |
Table 6.
Top 10 contributing authors of CRA research.
| Author | Affiliation | Country | TP | TC | PY_ Start |
|---|---|---|---|---|---|
| Sun Jie | School of Management, Harbin Institute of Technology | China | 9 | 646 | 2008 |
| Tsai CF | Department of Information Management, National Central University | Taiwan | 8 | 843 | 2009 |
| Li Hui | Nankai University | China | 8 | 646 | 2008 |
| Shi Yong | University of Chinese Academy of Sciences | China | 5 | 228 | 2010 |
| Du Jardin Philippe | Edhec Business School | France | 5 | 216 | 2010 |
| Virag Miklos | Corvinus University of Budapest | Hungary | 5 | 121 | 2012 |
| Liang Deron | National Central University | Taiwan | 4 | 477 | 2015 |
| Mues Christophe | University of Southampton | UK | 4 | 215 | 2012 |
| Ribeiro Bernardete | University of Coimbra | Portugal | 4 | 194 | 2016 |
| Petropoulos Anastasios | Bank of Greece | Greece | 4 | 102 | 2020 |
Note: TP = total publications; TC = total citations; PY_start = year of first CRA publication.
Table 7.
Top 10 corresponding authors’ countries by TP, SCP, and MCP.
| Sr. No. | Country | TP | SCP | MCP | MCP_Ratio |
|---|---|---|---|---|---|
| 1 | China | 212 | 159 | 53 | 25.00% |
| 2 | India | 109 | 94 | 15 | 13.76% |
| 3 | USA | 77 | 48 | 29 | 37.66% |
| 4 | United Kingdom | 51 | 20 | 31 | 60.78% |
| 5 | Spain | 46 | 34 | 12 | 26.09% |
| 6 | Italy | 37 | 20 | 17 | 45.95% |
| 7 | France | 27 | 18 | 9 | 33.33% |
| 8 | Australia | 25 | 18 | 7 | 28.00% |
| 9 | Poland | 25 | 20 | 5 | 20.00% |
| 10 | Iran | 24 | 21 | 3 | 12.50% |
Note: SCP = single-country publications; MCP = multiple-country publications; MCP_Ratio = ratio of internationally co-authored publications.
Table 8.
Top 10 affiliations by scientific production in CRA research.
| Rank | Affiliation | Country | 2025 Articles | Total Articles | per Year Article | PY_ Start |
|---|---|---|---|---|---|---|
| 1 | Islamic Azad University | Iran | 25 | 108 | 8.3 | 2013 |
| 2 | Egyptian Knowledge Bank | Egypt | 21 | 54 | 5.4 | 2016 |
| 3 | Min. of Education and Science | Ukraine | 21 | 59 | 9.8 | 2020 |
| 4 | University of London | United Kingdom | 17 | 53 | 8.8 | 2020 |
| 5 | Chinese Academy of Sciences | China | 15 | 97 | 6.1 | 2010 |
| 6 | Bucharest Uni. of Econ. Studies | Romania | 14 | 79 | 6.1 | 2013 |
| 7 | National Central University | Taiwan | 14 | 141 | 8.3 | 2009 |
| 8 | National Institute of Technology | India | 14 | 55 | 7.9 | 2019 |
| 9 | European Central Bank | Eurozone | 12 | 46 | 7.7 | 2020 |
| 10 | University of Granada | Spain | 12 | 50 | 7.1 | 2019 |
Note: The European Central Bank operates across Eurozone countries and is headquartered in Frankfurt, Germany.
Table 9.
The most influential co-authorship authors in the collaboration networks of CRA in banking.
Table 9.
The most influential co-authorship authors in the collaboration networks of CRA in banking.
| Cluster/Color | Author | Key Metrics (Links/Docs/Cit.) | Indicative Subfield |
|---|---|---|---|
| 1/Red | Hassan, M. Kabir/Rabbani, Mustafa Raza | 12/3/45, 5/2/21 | Recent Trends and Applications in Financial AI |
| 2/Green | Chi, Guotai/Habib, Tabassum | 11/5/35, 8/3/13 | Credit Risk Modeling and Financial Inclusion |
| 3/Blue | Chen, Zhensong/Qu, Yi | 7/2/12, 7/2/12 | DL and Advanced Predictive Analytics |
| 4/Yellow | Wang, Jing/Long, Xingchen | 10/4/51, 4/1/4 | Financial Analysis and Decision Making with Advanced Models |
| 5/Purple | Abedin, Mohammad Zoynul/Fahmida-emoula | 12/4/84, 5/2/30 | ML and Data-Driven Insights |
| 6/Turquoise | Shi, Yong/He, Jing | 11/4/228, 3/1/9 | Computational Finance and Optimization |
| 7/Orange | Shi, Baofeng/Meng, Bin | 8/2/79, 4/1/47 | Interdisciplinary Applications of AI and Data Science |
| 8/Brown | Zhou, Ying/Bai, Fengshan | 10/4/17, 3/1/0 | Emerging Trends and Methodological Innovations |
| 9/Pink | Lu, Yang/Liu, Xiaohui | 7/2/40, 5/2/129 | Quantitative Methods and Algorithmic Trading |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Trigkas, S.J.; Toudas, K.; Chasiotis, I. Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study. Computation 2025, 13, 172. https://doi.org/10.3390/computation13070172
AMA Style
Trigkas SJ, Toudas K, Chasiotis I. Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study. Computation. 2025; 13(7):172. https://doi.org/10.3390/computation13070172
Chicago/Turabian StyleTrigkas, Sotirios J., Kanellos Toudas, and Ioannis Chasiotis. 2025. "Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study" Computation 13, no. 7: 172. https://doi.org/10.3390/computation13070172
APA StyleTrigkas, S. J., Toudas, K., & Chasiotis, I. (2025). Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study. Computation, 13(7), 172. https://doi.org/10.3390/computation13070172
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
