Defined as a computer system designed to perform tasks that usually require human intelligence, artificial intelligence (AI) has been moving to the centre stage of financial applications. The module will provide you with the principles for understanding how finance has become integrated into computer system design in order to construct algorithms that provide optimal financial decision-making. Building on standard financial theory, the module will introduce you to the modern machine-learning techniques and methods (such as supervised learning, unsupervised learning, and reinforcement learning) in financial applications such as credit risk modelling and stock price analysis. The module is based on a data driven approach in which the machine learning techniques, using programming languages such as Python, are implemented using either simulated or real data.

Learning Outcomes

By the end of this module, you will:

have a deep understanding on how AI is applied in finance. In particular, you know how machine learning methods can impact financial modelling and understand the differences between applying machine learning in finance and in engineering.
	learn different machine learning techniques (like supervised and unsupervised learning) and their applications in financial modelling such as portfolio and credit risk modelling. For instance, you will be able to estimate the probability of a bank failure or defaultable loans.
	know how to apply and analyse AI algorithms on real financial data with Python.
	gain practical grounding in machine learning for decision-making and its applications in finance and banking.
Reading List:

The module is based on a wide variety of references, but the following three references may be consulted to start with:

De Prado, M.L., 2018. Advances in financial machine learning. John Wiley & Sons.
	Géron, A., 2017. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.".