Topic Editors

Department of Management, University of Brasília, Brasilia 70904-104, Brazil
Centro de Ciências Sociais e Aplicadas (CCSA), Campus Higienópolis, Universidade Presbiteriana Mackenzie, Sao Paulo, Brazil

Artificial Intelligence, Banking, and Financial Risk Management

Abstract submission deadline
31 October 2026
Manuscript submission deadline
31 December 2026
Viewed by
994

Topic Information

Dear Colleagues,

The growing presence of Artificial Intelligence (AI) in banking and finance is transforming the way we think about decisions, from assessing credit and managing portfolios to promoting transparency and meeting regulatory standards. AI is reshaping models and mindsets, thereby creating new possibilities in the finance sector. However, it also raises important questions about trust, fairness, and accountability.

This Topic invites the submission of contributions that reflect on both the opportunities and challenges of using AI in financial decision-making. We particularly welcome studies on algorithmic risk management, explainable and responsible AI, fairness and bias mitigation, AI-based compliance and supervision, and systemic risk modeling. Research that connects AI to broader goals such as sustainable finance, financial inclusion, and ethical governance is also highly encouraged.

By bringing together perspectives from finance, economics, data science, regulation, and professional practice, this Topic seeks to inspire a deeper and more responsible understanding of how AI can shape the future of financial systems, helping them to become not only smarter, but also fairer, more transparent, and more human-centered.

Prof. Dr. Herbert Kimura
Prof. Dr. Leonardo Fernando Cruz Basso
Topic Editors

Keywords

  • artificial intelligence
  • machine learning
  • banking
  • regulation
  • FinTech
  • financial risk management
  • risks in AI
  • Algorithmic Fairness
  • explainable AI
  • responsible innovation

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Sustainability
sustainability
3.3 7.7 2009 17.9 Days CHF 2400 Submit
International Journal of Financial Studies
ijfs
2.2 4.6 2013 19.7 Days CHF 1800 Submit
Risks
risks
1.5 5.0 2013 20 Days CHF 1800 Submit
Journal of Risk and Financial Management
jrfm
- 5.0 2008 18.8 Days CHF 1600 Submit
Accounting and Auditing
accountaudit
- - 2025 15.0 days * CHF 1000 Submit
Economies
economies
2.1 4.7 2013 23.1 Days CHF 1800 Submit

* Median value for all MDPI journals in the second half of 2025.


Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

  1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
  2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
  5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (1 paper)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
24 pages, 3551 KB  
Article
Financial Performance Outcomes of AI-Adoption in Oil and Gas: The Mediating Role of Operational Efficiency
by Eldar Mardanov, Inese Mavlutova and Biruta Sloka
J. Risk Financial Manag. 2026, 19(1), 44; https://doi.org/10.3390/jrfm19010044 - 6 Jan 2026
Viewed by 584
Abstract
The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. Although Artificial Intelligence (AI) is widely promoted as a lever for profitability, the mechanisms through which AI adoption translate into financial outcomes remain [...] Read more.
The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. Although Artificial Intelligence (AI) is widely promoted as a lever for profitability, the mechanisms through which AI adoption translate into financial outcomes remain insufficiently specified in the oil and gas literature. Grounded in the Resource-Based View and Technology Adoption Theory, this study combines bibliometric mapping of 201 Scopus-indexed publications (2010–2025) with a focused comparative case analysis of important players (BP and Shell), based on publicly reported operational and financial indicators (e.g., operating cost, uptime-related evidence, and return on average capital employed—ROACE). Keyword co-occurrence analysis identifies five thematic clusters showing that efficiency-oriented AI use cases (optimization, automation, predictive maintenance, and digital twins) dominate the research landscape. A thematic synthesis of five highly cited studies further indicates that AI-enabled operational improvements are most consistently linked to measurable cost, productivity, or revenue effects. Case evidence suggests that large-scale predictive maintenance and digital twin programs can support capital efficiency by reducing unplanned downtime and structural costs, contributing to more resilient ROACE trajectories amid price swings. Overall, the findings support a conceptual pathway in which operational efficiency is a primary channel through which AI can create financial value, while underscoring the need for future firm-level empirical mediation tests using standardized KPIs. Full article
Show Figures

Figure 1

Back to TopTop