Machine Learning for Financial Products Recommendation.

Summary Anticipating client needs is crucial for any company - this is especially true for investment banks such as BNP Paribas Corporate and Institutional Banking given their role in the financial markets. This thesis focuses on the problem of predicting future customer interests in the financial markets, with a particular emphasis on the development of ad hoc algorithms designed to solve specific problems in the financial world.This manuscript consists of five chapters, divided as follows:- Chapter 1 presents the problem of predicting future customer interests in the financial markets. The purpose of this chapter is to provide the reader with all the keys necessary for a good understanding of the rest of this thesis. These keys are divided into three parts: a highlighting of the datasets available to us for solving the future interest prediction problem and their characteristics, a non-exhaustive overview of the algorithms that can be used to solve this problem, and the development of metrics to evaluate the performance of these algorithms on our datasets. This chapter closes with the challenges that can be encountered when designing algorithms to solve the problem of predicting future interests in finance, challenges that will be, in part, solved in the following chapters: - Chapter 2 compares some of the algorithms introduced in Chapter 1 on a dataset from BNP Paribas CIB, and highlights the difficulties encountered when comparing algorithms of different nature on the same dataset, as well as some ways to overcome these difficulties. This comparison puts into practice classical recommendation algorithms only considered from a theoretical point of view in the previous chapter, and allows us to acquire a more detailed understanding of the different metrics introduced in chapter 1 through the analysis of the results of these algorithms. Chapter 3 introduces a new algorithm, Experts Network, i.e., a network of experts, designed to solve the problem of heterogeneous behavior of investors in a given market through an original neural network architecture, inspired by research on expert mixtures. In this chapter, this new methodology is used on three distinct datasets: a synthetic dataset, an open access dataset, and a dataset from BNP Paribas CIB. Chapter 4 also introduces a new algorithm, called History-augmented collaborative filtering, which proposes to augment the classical matrix factorization approaches with the help of the interaction histories of the considered customers and products. This chapter continues the study of the dataset studied in Chapter 2 and extends the introduced algorithm with many ideas. Specifically, this chapter adapts the algorithm to address the cold start problem, i.e., the inability of a recommender system to provide predictions for new users, as well as a new application case on which this adaptation is tried.- Chapter 5 highlights a collection of ideas and algorithms, both successful and unsuccessful, that have been tried in the course of this thesis. This chapter closes with a new algorithm combining the ideas of the algorithms introduced in chapters 3 and 4.