Interpretable Machine Learning forFinancial Applications

Abstract

This chapter describes machine learning (ML) for financial applications with a focus on interpretable relational methods. It presents financial tasks, methodologies, and techniques in this ML area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, and attribute-based and interpretable relational methodologies. The second part of this chapter presents ML models and practice in finance. It covers the use of ML in portfolio management, design of interpretable trading rules, and discovering money-laundering schemes using the machine learning methodology.