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Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial...
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Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial Systems

A Special Issue of Journal of Risk and Financial Management (ISSN 1911-8074) belonging to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1151

Editor

Dr. Issam Benhayoun

E-Mail Website
Guest Editor
The National School of Business and Management, Moulay Ismail University (ENCG-Meknes), Meknes 52202, Morocco
Interests: sustainability reporting; digital accountability; AI in auditing; financial governance; ESG disclosure; fraud detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid transformation of financial systems driven by artificial intelligence, financial technologies, and sustainability imperatives is redefining the landscape of global finance. This Special Issue, linked to CIRM’26–Track 4: Finance, Governance, and Digital Accountability, aims to explore how emerging technologies and new governance models shape financial decision-making, transparency, inclusion, and long-term value creation.

Key areas of interest include the integration of AI in auditing, control, and financial decision-making; the rise of Fintech solutions that promote financial inclusion and reduce inequality; advances in green finance and impact investing; and the emergence of algorithmic governance in both corporate and public policy contexts. These themes are strongly aligned with JRFM’s aims and scope, particularly its focus on financial innovation, digital transformation, risk management, and sustainable finance.

We welcome high-quality submissions that advance theoretical, empirical, or methodological understanding of how finance and governance are being reconstructed in the digital era. Submissions may originate from the CIRM’26 conference or be independent contributions, provided they offer original insights into the intersection of finance, technology, and sustainability. Expanded versions of selected conference papers are also encouraged.

This Special Issue offers an excellent opportunity for scholars to disseminate innovative research that contributes to the future of financial accountability and digital governance.

Dr. Issam Benhayoun
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in finance
  • digital accountability
  • algorithmic governance
  • fintech and financial inclusion
  • audit analytics
  • green finance
  • sustainable investing
  • ESG and digital reporting

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Published Papers (3 papers)

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32 pages, 599 KB  
Article
Exploring a Potential Capability–Adoption Gap in Digital Financial Adoption Intentions: Evidence from a Fragile Post-Crisis Economy
by Jeanne Laure Mawad, Nouhad Mawad and Anthony Nehme
J. Risk Financ. Manag. 2026, 19(9), 676; https://doi.org/10.3390/jrfm19090676 - 3 Sep 2026
Viewed by 220
Abstract
Digital finance research generally assumes that greater financial and technological capabilities lead to higher adoption of digital financial services. However, this assumption has rarely been examined in fragile economies where institutional trust and financial system stability have been severely disrupted. This study investigates [...] Read more.
Digital finance research generally assumes that greater financial and technological capabilities lead to higher adoption of digital financial services. However, this assumption has rarely been examined in fragile economies where institutional trust and financial system stability have been severely disrupted. This study investigates whether capability-based explanations of digital financial adoption remain valid in a post-crisis environment. Drawing on a capability–behaviour–intention framework, the study examines the effects of Perceived Digital Financial Literacy (DFL), Perceived AI Literacy (AIL), and Financial Inclusion (FI) on Digital Financial Adoption Orientation (DFAO), while assessing the mediating role of Financial Behaviour (FB) and the moderating influence of banking reintegration and bank stability concerns. The study analyzed questionnaire responses from 164 participants using partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4. The findings provide mixed support for the proposed framework. AIL and FI were positively associated with FB, whereas the direct relationships of DFL, AIL, and FI with DFAO were not statistically significant. FB was not significantly associated with DFAO, and the specific indirect effects through FB were not statistically supported. Fresh USD account ownership significantly moderated the relationship between FI and DFAO, whereas the remaining proposed moderation effects were not statistically significant. The observed pattern provides limited support for the hypothesised capability–behaviour–adoption relationships in this sample and suggests the possibility of boundary conditions affecting capability-based explanations of digital financial adoption. Further research using larger and more diverse samples is required to determine whether these relationships vary across contexts. Full article
(This article belongs to the Special Issue Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial Systems)
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55 pages, 5137 KB  
Systematic Review
Predicting, Using, and Assessing ESG Signals: A Tripartite Systematic Review of Machine Learning in Sustainable Finance
by Imane El Imami, Abdelkader El Alaoui, Bassma Guermah, Said Ouatik El Alaoui and Miklos Vasarhelyi
J. Risk Financ. Manag. 2026, 19(9), 708; https://doi.org/10.3390/jrfm19090708 - 8 Sep 2026
Abstract
Environmental, Social, and Governance (ESG) ratings increasingly shape capital allocation, corporate strategy, and regulatory oversight, yet their credibility is constrained by methodological opacity, rating divergence, and greenwashing risk. Prior reviews treat machine learning (ML) in ESG as a prediction problem. We identify an [...] Read more.
Environmental, Social, and Governance (ESG) ratings increasingly shape capital allocation, corporate strategy, and regulatory oversight, yet their credibility is constrained by methodological opacity, rating divergence, and greenwashing risk. Prior reviews treat machine learning (ML) in ESG as a prediction problem. We identify an emerging research trajectory in which ML is increasingly used not only to consume ESG signals but also to verify their construction and credibility. Drawing on signaling theory, we conduct a PRISMA-guided systematic review of 127 peer-reviewed studies from Scopus and Web of Science to examine how machine learning (ML), deep learning (DL), Natural Language Processing (NLP), and Explainable AI (XAI) are transforming ESG rating analysis. We develop a tripartite framework classifying studies by the functional role of the ESG score: predicted (n = 29), used (n = 57), or assessed (n = 41). Our central contribution is the first synthesis of the methodological-assessment stream, organized into four clusters: XAI reverse-engineering of proprietary scoring functions, divergence reconciliation, greenwashing detection, and unsupervised industry-materiality clustering. The evidence assembled in this stream indicates that ESG ratings weight low-cost aspirational disclosure heavily relative to costly performance evidence, suggesting that greater reliance on aspirational disclosure relative to performance evidence may increase greenwashing risk, consistent with signaling-theory concerns. A study-level validation appraisal further shows that the most extreme fit statistics often arise in target-proximal reconstruction or non-temporal validation settings, cautioning against interpreting high R2 as evidence of transferable out-of-time forecasting. Full article
(This article belongs to the Special Issue Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial Systems)
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22 pages, 2060 KB  
Systematic Review
Mapping the Methodological Bifurcation of Quantitative Portfolio Optimization: A PRISMA-Compliant Systematic Review with BERTopic–SPECTER Analysis (2003–2025)
by Gharmili Meryem, Boudri Imane and Alj Abdelkamel
J. Risk Financ. Manag. 2026, 19(8), 582; https://doi.org/10.3390/jrfm19080582 - 3 Aug 2026
Viewed by 359
Abstract
Quantitative portfolio optimization has accelerated sharply since 2018, with deep learning and reinforcement learning agents now competing with the mean–variance framework that defined six decades of research. Existing narrative reviews struggle to track this expansion. We screen 832 documents from Scopus and Web [...] Read more.
Quantitative portfolio optimization has accelerated sharply since 2018, with deep learning and reinforcement learning agents now competing with the mean–variance framework that defined six decades of research. Existing narrative reviews struggle to track this expansion. We screen 832 documents from Scopus and Web of Science under PRISMA 2020 and retain 589 unique articles spanning 2003–2025. Applying BERTopic with SPECTER scientific embeddings, UMAP and HDBSCAN, we identify five coherent topics with a mean coherence of 0.864: classical mean–variance (T0; n = 270), deep reinforcement learning (T1; n = 116), machine learning return forecasting (T2; n = 87), covariance estimation and robust optimization (T3; n = 52)—and metaheuristics (T4; n = 56). A rank-weighted similarity analysis, designed to neutralise the c-TF-IDF collinearity artefact, shows that deep reinforcement learning is the most isolated paradigm. The two methodological families bifurcate over time: AI/deep learning approaches grow from 3.6% of annual output before 2018 to 40.2% afterwards, while classical methods retain volume but lose share. We synthesise the empirical practices of each family along five dimensions critical to applied finance and identify three under-explored integration frontiers. Full article
(This article belongs to the Special Issue Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial Systems)
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