Introduction on AI Approaches in Capital Markets

Summary

This chapter introduces the key considerations, trends and use cases for artificial intelligence (AI) in capital markets. It is meant for non‐specialists, and, in particular, decision‐makers needing to understand the benefits, pitfalls, and how to approach AI projects in the financial domain. Of all the subfields of AI, arguably one of the most popular is machine learning (ML), which applies statistical models to data in order to learn from them and make predictions. The professional field most closely linked to AI, and especially ML, is that of the data scientist. One of the most important tasks that a data scientist can perform, in collaboration with stakeholders, is that of translating a business problem to a specification for the AI‐based system. The key use cases for AI in capital markets can be split into four groups: customer, trading/portfolio management, regulatory, and operations.