THEME: SUPTECH, DATA SCIENCE, AND THE PARADIGM SHIFTS IN FINANCIAL SUPERVISION
- Submission deadline: November 13, 2023
- Notification of acceptance: November 22, 2023
- Forum dates: December 4-7, 2023
About SupTech Week:
SupTech Week is a global forum for sharing ideas, research, solutions and insights on the digital transformation of financial supervision, fostering collaboration across the suptech ecosystems.
Suptech Week is powered by the Cambridge SupTech Lab at the Cambridge Centre for Alternative Finance (CCAF) in collaboration with the Bank for International Settlements Innovation Hub and the World Economic Forum (content partners).
Topics of interest:
We invite submissions covering a diverse range of topics related to the paradigm shifts in financial supervision due to the digitization, datafication, globalization and decentralization of financial services. Topics should focus on the potential to foster innovation, inclusiveness, accountability and resiliency in financial supervision by deploying new methodologies and suptech applications. Suggested topics include but are not limited to:
- Suptech innovations: Explore the latest technological advancements in supervisory tools, data analytics, and artificial intelligence for enhanced supervisory purposes.
- Fintech integration: Investigate the integration of fintech innovations, including decentralized finance (DeFi), and digital assets, into supervisory frameworks.
- Data management: Examine effective strategies for effective data collection, validation, and analysis in a suptech-driven environment.
- Data accessibility and privacy: Examine strategies for ensuring equitable access to financial data while safeguarding privacy and security.
- Big data, machine learning and predictive analytics: Investigate the use of big data, machine learning and predictive analytics in identifying potential risks and anomalies in financial markets and institutions.
- Natural Language Processing (NLP): Investigate the use of NLP techniques to analyze and extract insights to inform supervisory decisions.
- Anomaly detection in transaction data: Examine the application of data science methods, including anomaly detection algorithms, in identifying suspicious or fraudulent transactions.
- Network analysis: Present network analysis techniques to assess the interconnectedness of financial institutions and transactions, the risks generated in the financial system, and their identification and evaluation by financial supervisors.
- Predictive modeling for fraud detection: Investigate predictive modeling approaches to detect financial frauds and crimes.
- Web scraping for market surveillance: Explore the use of web scraping techniques to monitor and extract real-time data from financial news websites, social media platforms, and other online sources to identify emerging trends, market sentiment, and potential risks, complementing traditional data sources in financial supervision.
- Time series analysis for market surveillance: Investigate the use of time series analysis to monitor and predict market trends, volatility, and potential anomalies in financial markets.
- Machine learning in credit scoring: Discuss the use of machine learning models in credit scoring, including alternative data sources for credit assessment, and the identification of algorithmic biases.
- Experiential data in financial supervision: Investigate the potential of experiential data, such as user reviews and feedback, in assessing the quality of financial products and services.
- Explainable AI (XAI) in financial supervision: Explore methods for making AI models more transparent and interpretable, addressing the need for financial authorities to understand and justify AI-driven decisions.
- Data governance and quality assurance: Discuss the importance of robust data governance practices and quality assurance processes in maintaining the integrity of financial data used for supervision.
- Data privacy and compliance in financial data analytics: Analyzse the challenges and solutions for data privacy and compliance with regulations such as GDPR and CCPA in the context of financial data analytics.
- Data integration and aggregation for the supervisory data stack: Examine techniques for integrating and aggregating data from diverse sources, such as fintech startups, traditional banks, third-party providers, and open data sets to support supervision.
- Data visualization and dashboards for supervisory insights: Explore effective data visualization techniques and dashboard designs for providing supervisors with actionable insights.
- Cybersecurity: Investigate the critical aspects of cybersecurity in the context of deploying suptech and the safeguarding of sensitive data sets.
- Ethical and legal considerations: Analyse the ethical and legal implications of utilizing suptech tools in supervisory practices.
- Case studies: Share real-world case studies of suptech implementation in financial authorities.
- Proofs of concept (POCs): Preliminary project or experiment documentation that demonstrates the feasibility and practicality of an innovative idea or technology.
- Submissions should be in PDF format.
- Papers should be between 6-8 pages in length, (not inclusive of references and figures, which can be added as annexes).
- All submissions will undergo a double-blind peer review process.
- Accepted papers will be published in the forum proceedings.
- Selected authors of high-quality papers might be invited to present them during Suptech Week.
- There will be no submission/registration fees for submission of papers.
- Diverse submissions welcome: We encourage a diverse spectrum of submissions for Suptech Week 2023 to ensure a comprehensive exploration of the field. We welcome contributions that span the academic-practical continuum, including:
- Academic style papers: These submissions should follow traditional academic research guidelines and contribute to the theoretical foundations of suptech, data science, and financial supervision. They should present original research, comprehensive literature reviews, and rigorous methodologies.
- Practical case studies: We invite submissions that showcase real-world applications of suptech in financial supervision. Case studies provide insights into successful implementations, challenges faced, and lessons learned. They offer practical guidance for practitioners and policymakers.
- Proofs of Concept (POCs): POC submissions focus on innovative suptech applications that are still in the experimental or developmental stage. They should demonstrate the feasibility and potential of new suptech tools, data-driven approaches, or emerging technologies.
- Douglas Arner,Kerry Holdings Professor in Law, The University of Hong Kong
- Louis de Koker, Professor of Law, La Trobe Law School
- Lucio Sarno, Professor of Finance, University of Cambridge
- Minerva Tantoco, Former CTO, city of New York
- Nydia Remolina Leon, Assistant Professor of Law, Singapore Management University
- Raffaella Calabrese, Chair of Statistics and Data Science, University of Edinburgh
- , Sir Evelyn de Rothschild Professor of Finance, University of Cambridge
- Robert Wardrop, Centre Director, Cambridge Centre for Alternative Finance, Cambridge Judge Business School
For any inquiries or assistance regarding paper submissions, please contact firstname.lastname@example.org.
Visit the forum website for updates and further information: www.suptechweek.org.
Join us in advancing suptech and shaping the future of financial supervision. We eagerly await your contributions to SupTech Week 2023!