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

Track 1: 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 -

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鈥檚 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. .
• Machine learning in UK financial services. (2019).
• 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).

Track 2: Systemic Trustworthiness in Digital Infrastructure: Evidence, Threats, and Assurance at Population Scale

Digital Public Infrastructure (DPI) and AI-enabled systems increasingly underpin the foundations of modern digital economies, supporting digital identity systems, payments, data exchange, and public service delivery. Governments and international organisations are investing heavily in DPI as a mechanism for improving service efficiency, financial inclusion, and economic participation. The World Bank describes DPI as foundational digital building blocks enabling trusted digital transactions through identity, payments, and data exchange systems.鹿 Similarly, the United Nations Development Programme highlights the role of DPI in enabling governments to deliver services securely and efficiently across public and private sectors.虏

However, trustworthiness in population-scale digital systems cannot be reduced to technical standards, cryptography, or regulatory compliance alone. Research on digital governance and infrastructure security increasingly frames trustworthiness as a systemic property emerging from the interaction of technical architectures, governance frameworks, operational security practices, and evolving threat environments.鲁 The rapid integration of artificial intelligence into digital infrastructure further complicates this landscape. AI systems are increasingly used for fraud detection, identity verification, and automated service delivery, while regulators warn that widespread AI adoption may introduce systemic vulnerabilities, including correlated model failures and new cyber-security attack surfaces.鈦�

At the same time, digital identity platforms, payment networks, and government digital services are becoming increasingly attractive targets for organised cybercrime and sophisticated adversaries. The growing interconnection between identity systems, financial infrastructures, and digital service platforms creates new forms of cross-system risk, where vulnerabilities in one infrastructure component can propagate across others.鈦� This workshop brings together empirical threat intelligence from the Alan Turing Institute鈥檚 Cyber Threat Observatory with interdisciplinary research on cybersecurity, governance, and AI risk, with the aim of advancing research and collaboration on systemic trustworthiness in digital infrastructure at population scale.

Contributions may include empirical studies, early-stage research, design proposals, and conceptual work. Relevant topics include, but are not limited to, the following thematic areas:

1. Security Architectures for Digital Public Infrastructure
   Design and evaluation of secure architectures for digital identity, payments, and public service platforms; secure APIs, zero-trust approaches, and system-level threat modelling for population-scale infrastructure.
2. Cyber Threat Intelligence and Real-World Attacks
   Empirical studies of cyber threats targeting DPI, including attacks on identity systems, payment infrastructures, registries, and digital government services; analysis of adversary tactics, techniques, and procedures.
3. Cross-System Risk and Infrastructure Interdependencies
   Research examining risk propagation across interconnected digital systems, including identity–payments–data exchange ecosystems and cascading failures across digital economy platforms.
4. AI, Automation, and Emerging Threat Surfaces
   AI-enabled fraud, automated scams, adversarial attacks, and manipulation of AI-assisted decision systems; risks introduced by machine learning and generative AI embedded in critical digital infrastructure.
5. Assurance, Accountability, and Evidence Frameworks
   Methods for demonstrating trustworthiness in large-scale digital systems, including assurance models, auditing frameworks, transparency mechanisms, and evidence-based approaches to system governance.
6. Governance, Regulation, and Institutional Design
   Policy frameworks, regulatory approaches, and governance models for digital public infrastructure and AI-enabled public services, including international regulatory comparisons and institutional accountability mechanisms.
7. Cryptographic Foundations and Hardware Trust Anchors
   Trusted hardware, secure enclaves, hardware security modules, post-quantum cryptography, and crypto-agility strategies for long-lived public digital infrastructure.
8. Privacy, Identity, and Responsible Data Use
   Privacy-preserving technologies, identity assurance mechanisms, biometric risks, data governance frameworks, and approaches to balancing privacy, security, and usability in large-scale digital systems.
9. Socio-Economic Impacts and Global Deployment Challenges
   Digital inclusion, accessibility, and equity in digital public infrastructure; implementation challenges in low- and middle-income countries; and the broader societal implications of population-scale digital systems.

References:

1. World Bank – Digital Public Infrastructure and Development
   
2. UNDP – Digital Public Infrastructure
   
3. OECD – Digital Public Infrastructure for Digital Governments
   
4. Financial Stability Board – The Financial Stability Implications of Artificial Intelligence
   
5. World Bank – Interoperable Digital Public Infrastructure and Economic Development