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Data and Technology: Shaping the Future of Finance, Accounting, and...
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Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 18998

Special Issue Editors

Prof. Dr. Chuleeporn Changchit

E-Mail Website
Guest Editor
Department of Decision Sciences and Economics, College of Business, Texas A&M University-Corpus Christi, 6300 Ocean Dr., Corpus Christi, TX 78412, USA
Interests: technology adoption; e-Commerce; mobile banking and payment; decision support systems; distance education; accounting information system
Dr. Ravi Lonkani

E-Mail Website
Guest Editor
Department of Finance, Faculty of Business Administration, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: managerial finance; behavioral finance; sustainability; ESG; financial innovation; financial information system and technology; investment

Special Issue Information

Dear Colleagues,

This Special Issue explores the transformative impact of sustainability, ethical practices, social media, information technology, innovation, data analytics, and AI on financial and accounting systems, as well as broader business applications. As businesses navigate the digital economy and adapt to a growing emphasis on sustainable development, the adoption of advanced technologies and sustainable practices has become crucial for enhancing business processes and decision-making.

We invite papers that examine the influence of cutting-edge technologies—such as social media, AI, and data analytics—on financial management, accounting systems, and overall business strategies. Topics of interest include, but are not limited to, the integration of AI in financial forecasting, the role of data analytics in strategic decision-making, the ethical implications of technology in finance, and the growing importance of sustainability in accounting practices.

This Special Issue seeks to bring together academic researchers, industry professionals, and thought leaders to provide a comprehensive view of how technology is shaping the future of finance, accounting, and business systems. Contributions that offer practical insights, theoretical advancements, or case studies demonstrating innovative applications are particularly welcome.

By fostering a deeper understanding of these emerging trends, this Special Issue aims to create a platform for thought-provoking discussions on how businesses can harness technology and innovation to drive sustainable growth and ethical financial practices in an increasingly complex global economy.

Prof. Dr. Chuleeporn Changchit
Dr. Ravi Lonkani
Guest Editors

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-blind 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

  • data analytics
  • financial technology (FinTech)
  • accounting innovation
  • business system transformation
  • artificial intelligence in finance
  • blockchain technology
  • cryptocurrency
  • big data
  • digital transformation
  • risk management systems
  • automation in accounting
  • machine learning applications
  • cloud-based business solutions
  • cybersecurity in financial systems
  • predictive analytics
  • business process optimization
  • innovation in financial services
  • data governance
  • decision support systems
  • enterprise resource planning (ERP)
  • regulatory technology (RegTech)
  • financial and accounting management
  • information technology
  • business innovation
  • social media
  • AI application in business
  • sustainability in finance and accounting
  • corporate ethics, governance, and accountability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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34 pages, 3394 KB  
Article
Predictive Valuation of Non-Fungible Tokens (NFTs): Machine Learning Models in Decentralized Finance
by Athanasios Kranias
J. Risk Financial Manag. 2026, 19(2), 126; https://doi.org/10.3390/jrfm19020126 - 7 Feb 2026
Viewed by 1190
Abstract
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, [...] Read more.
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear models—Random Forest, XGBoost, and a Multilayer Perceptron—are trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability. Full article
(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)
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32 pages, 3133 KB  
Article
Innovative Credit Risk Assessment: Leveraging Social Media Data for Inclusive Credit Scoring in Indonesia’s Fintech Sector
by Andry Alamsyah, Aufa Azhari Hafidh and Annisa Dwiyanti Mulya
J. Risk Financial Manag. 2025, 18(2), 74; https://doi.org/10.3390/jrfm18020074 - 2 Feb 2025
Cited by 16 | Viewed by 10791
Abstract
The financial technology domain has undertaken significant strides toward more inclusive credit scoring systems by integrating alternative data sources, prompting an exploration of how we can further simplify the process of efficiently assessing creditworthiness for the younger generation who lack traditional credit histories [...] Read more.
The financial technology domain has undertaken significant strides toward more inclusive credit scoring systems by integrating alternative data sources, prompting an exploration of how we can further simplify the process of efficiently assessing creditworthiness for the younger generation who lack traditional credit histories and collateral assets. This study introduces a novel approach leveraging social media analytics and advanced machine learning techniques to assess the creditworthiness of individuals without traditional credit histories and collateral assets. Conventional credit scoring methods tend to rely heavily on central bank credit information, especially traditional collateral assets such as property or savings accounts. We leverage demographics, personality, psycholinguistics, and social network data from LinkedIn profiles to develop predictive models for a comprehensive financial reliability assessment. Our credit scoring methods propose scoring models to produce continuous credit scores and classification models to categorize potential borrowers—particularly young individuals lacking traditional credit histories or collateral assets—as either good or bad credit risks based on expert judgment thresholds. This innovative approach questions conventional financial evaluation methods and enhances access to credit for marginalized communities. The research question addressed in this study is how to develop a credit scoring mechanism using social media data. This research contributes to the advancing fintech landscape by presenting a framework that has the potential to transform credit scoring practices to adapt to modern economic activities and digital footprints. Full article
(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)
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Review

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22 pages, 341 KB  
Review
The Role of Artificial Intelligence in Enhancing ESG Disclosure Quality in Accounting
by Jiacheng Liu, Ye Yuan and Zhelun Zhu
J. Risk Financial Manag. 2026, 19(1), 58; https://doi.org/10.3390/jrfm19010058 - 9 Jan 2026
Cited by 3 | Viewed by 2662
Abstract
As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics [...] Read more.
As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in this domain. We delineate ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations (e.g., transformer-based models for semantic analysis), empirical linkages between AI-extracted signals and market/governance outcomes, and normative discussions on AI’s auditing potential, we demonstrate AI’s efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. Nonetheless, causal evidence linking managerial AI adoption to stakeholder-perceived enhancements remains limited, compounded by biases in multilingual applications and interpretability deficits. We propose a forward-looking agenda, prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards, to harness AI for substantive, equitable improvements in ESG reporting and accountability. Full article
(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)

Other

Jump to: Research, Review

33 pages, 2389 KB  
Systematic Review
Integration of Blockchain in Accounting and ESG Reporting: A Systematic Review from an Oracle-Based Perspective
by Giulio Caldarelli
J. Risk Financial Manag. 2025, 18(9), 491; https://doi.org/10.3390/jrfm18090491 - 3 Sep 2025
Cited by 3 | Viewed by 3565
Abstract
The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to [...] Read more.
The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to improve legacy accounting and reporting systems. However, integrating real-world data into a blockchain requires the use of oracles: third-party systems that, if poorly selected, may be less decentralized and transparent, potentially undermining the expected benefits. Through a systematic review of the existing literature, this study investigates whether research articles on the integration of blockchain technology in accounting and reporting have addressed the limitations posed by oracles, under the rationale that the omission of oracles constitutes a theoretical bias. Furthermore, this study examines oracle-based solutions proposed for reporting applications and classifies them based on their intended purpose. While the overall consideration of oracles remains limited, the findings indicate a steadily increasing interest in their role and implications within accounting, auditing, and ESG-related blockchain implementations. This growing attention is particularly evident in ESG reporting, where permissioned blockchains and attestation mechanisms are increasingly being examined as practical responses to data verification challenges. Full article
(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)
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