Special Tracks

Special Tracks for the International Conference for AI and the Digital Economy 2026

FinTech: Responsible Deployment of Frontier AI

Session organisers – Carsten Maple, Anita Khadka, Geetika Jain, Dimitrios Kafteranis

Background/motivation – The responsible deployment of frontier AI in financial services is among the most pressing challenges facing regulators, institutions, and researchers today. The special track addresses the risk of hallucinated financial advice, developing practical auditing frameworks to measure the stability and reliability of AI-generated outputs, whilst systematically comparing regulatory approaches across the EU, UK, and US. This track provides precisely the interdisciplinary platform that translates the research into policy-relevant impact demands, and it is this gap between what frontier AI can do in finance and what it should do that fundamentally motivates the research from the perspective of Responsible AI. The special track is linked with the RAISE-Fin project - Responsible Deployment of Frontier AI in Financial Services: Policy Pathways and Risk Frameworks - UKFin+

Special session description (this will be used of the CADE website if application is successful) - FinTech is rapidly reshaping the financial services industry at an unprecedented pace (Gomber et al., 2018). The simultaneous emergence of large language models (LLMs), generative AI (GAI), autonomous agents, and advanced machine learning systems has accelerated this transformation across all domains of finance (Leveraging Large Languauge Models in Finance: Pathways to Responsible Adoption, 2025). Application areas include algorithmic trading, credit decisioning, fraud detection, customer service automation, regulatory compliance, risk assessment, and anti-money laundering (AML) (Sabuncuoglu, Burr, & Maple, 2025; Machine learning in UK financial services, 2019). Major financial institutions are investing billions in AI capabilities, while regulators worldwide—from the FCA in the UK to the European Commission and US federal agencies—are developing new frameworks to govern these powerful technologies (Kafteranis, 2025; Jain, Singh & Hashimy, 2025). Such developments have generated considerable interest in the critical research area of responsible AI deployment in financial services. FinTech continues to disrupt and reshape financial services while presenting novel risks that existing regulatory frameworks were not designed to address (Maple et al., 2023). The need to build AI governance competencies among practitioners, researchers, and regulators is apparent (Mirishli, 2025). Given the importance and challenges of deploying AI responsibly in finance, this track provides a platform for original studies on the topic. We particularly welcome submissions that bridge technical, regulatory, and socio-economic perspectives, including work at developmental stages and conceptual contributions.

AI governance frameworks and regulatory approaches
Model risk management for foundation models, LLMs, and generative AI systems
Explainability and fairness in AI-driven credit, insurance, and investment decisions
AI safety, adversarial robustness, and assurance methodologies
Consumer protection and vulnerability in AI-mediated financial services
Systemic risk implications of AI concentration and correlated model failures
Autonomous AI agents in trading, portfolio management, and market-making
AI-driven financial inclusion and access to services in underserved markets
Third-party AI dependencies, vendor risk, and supply chain considerations
Regulatory sandboxes and policy experimentation for AI in finance

The session will accept both extended abstracts and full papers. Selected paper will accepted based on the editor’s decision for the Edited Book (Taylor & Francis) and Journal Special Issue (Computers)

References:

Giudici, P. (2018). Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1, 1.
Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1), 220-265.
Jain, G., Singh, H., & Hashimy, L. (2025). Enhancing corporate governance with decentralized AI: a structuration theory perspective. Journal of Science and Technology Policy Management, 1-28.
Kafteranis, D. (2025). The whistle-blower as a private enforcement tool in the EU banking sector: call for clarity. Journal of Banking Regulation, 26(4), 627-635.
Leveraging Large Languauge Models in Finance: Pathways to Responsible Adoption (2025). European Securities and Markets Authority (ESMA), FaiR (Finance & Economics Reloaded) programme Institut Louis Bachelier, and FAIR (Framework for Responsible Adoption of Artificial Intelligence in the Financial Services Industry) programme at the The Alan Turing Institute. https://www.esma.europa.eu/sites/default/files/2025-06/LLMs_in_finance_-_ILB_ESMA_Turing_Report.pdf.
Machine learning in UK financial services. (2019). https://www.fca.org.uk/publication/research/research-note-on-machine-learning-in-uk-financial-services.pdf
Maple, C., Szpruch, L., Epiphaniou, G., Staykova, K., Singh, S., Penwarden, W., ... & Avramovic, P. (2023). The AI revolution: Opportunities and challenges for the finance sector. arXiv preprint arXiv:2308.16538.
Mirishli, S. (2025). Regulating AI in financial services: Legal frameworks and compliance challenges. arXiv preprint arXiv:2503.14541.
Sabuncuoglu, A., Burr, C., & Maple, C. (2025, June). Justified Evidence Collection for Argument-based AI Fairness Assurance. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency(pp. 18-28).