Proposal Description

Financial data science: opportunity and risks
Chair: Leonardo Gambacorta, Bank for International Settlements, Switzerland
Speakers:
Ying Chen, National University of Singapore, Republic of Singapore “Explainable and Fair artificial intelligence in finance”
Paolo Giudici, University of Pavia, Italy “Safe Artificial Intelligence in Finance”
Dale Rosenthal, Head Partner, Q36 Chicago, USA
Daniel Ahelegbey, African School of Economics and University of Essex, School of Mathematics, Statistics and Actuarial Science (SMSAS), UK “Network models in finance”

The growth of Artificial Intelligence applications, boosted by financial data science, requires developing risk assessment and management models that can balance opportunities with risks. The proposed session aims to contribute to the on-going debate on regulations and industry standards for AI risk management models, by discussing proposals of statistical models, statistical indicators and software codes that can be employed to measure the risks of AI.
The session will include research contributions from different regions and professional roles, to reflect diversity and to achieve a shared view. Academics will present their proposed risk management models and codes; financial regulators will talk about supervisory technology models (SupTech) for the regulation of AI in the financial sectors; financial professionals will illustrate how institutions can extend their risk management models to include the risks of AI.
This IPS is proposed by the ISBIS and the ISI Special Interest Group on Data Science.