Lecture
This lecture discusses the scope and limits of artificial intelligence in finance, focusing on defining artificial intelligence, the use of various statistical techniques, and the importance of interpretability in machine learning models. The panelists explore the application of tools like neural networks, reinforcement learning, and Bayesian techniques in finance, highlighting successful use cases such as fraud detection, prepayment prediction, and robo-advisors. They also address challenges such as the need for confidence intervals in point estimates, handling non-stationarity and trends in data, and the importance of understanding the underlying mechanisms behind data sets. The lecture concludes with a discussion on the impact of artificial intelligence on the finance industry and the shift towards data-driven decision-making.