Artificial Intelligence (AI) and Machine Learning (ML) are currently reshaping many domains, especially when natural pattern recognition is key. In the financial world, where game theory and decision making under uncertainty are keys, it has been known for a long time that humans consistently violate basic principles of rational choices. Could and should AI and ML understand, learn, and even take advantage of human cognitive weaknesses? We investigate two case studies related to over-confidence and loss aversion.
Director, Investment Data Platform
Yves Chauvin is the Director of the Investment Data Platform at AXA-IM, Rosenberg Equity. He is responsible for data processing from supplier data to data integration and for the construction of data quality tools used in computer models for asset management. He started work in Machine Learning more than 20 years ago, when Machine Learning was seen as an alternative statistical tool to extract relevant knowledge from noisy data. One current topic of interest is how recent advances in Machine Learning may change or add constraints to data processing and knowledge management in the financial industry.