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Mapping the Role of Artificial Intelligence and Machine Learning in Advancing Sustainable Banking

Author

Listed:
  • Alina Georgiana Manta

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Claudia Gherțescu

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Roxana Maria Bădîrcea

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Liviu Florin Manta

    (Faculty of Automation, Computers and Electronics, University of Craiova, 200585 Craiova, Romania)

  • Jenica Popescu

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Mihail Olaru

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

Abstract

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies.

Suggested Citation

  • Alina Georgiana Manta & Claudia Gherțescu & Roxana Maria Bădîrcea & Liviu Florin Manta & Jenica Popescu & Mihail Olaru, 2026. "Mapping the Role of Artificial Intelligence and Machine Learning in Advancing Sustainable Banking," Sustainability, MDPI, vol. 18(2), pages 1-44, January.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:2:p:618-:d:1835330
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