BERTRAND K. HASSANI | Université Paris 1 Panthéon-Sorbonne, University College London, and Partner, AI and Analytics, Deloitte

Though in the past, data captured by financial institutions and used to understand customers, processes, risks, and, more generally, the environment of financial institutions was mainly structured, i.e., sorted in “rigid” databases, today, that is no longer the case. Indeed, the so-called structured data is representing no more than a drop in an ocean of information. The objective of this paper is to present and discuss opportunities offered by natural language processing and understanding (NLP, NLU) to analyze the unstructured data, and automate its treatment. Indeed, NLP and NLU are essential to understanding and analyzing banks’ internal way of functioning and customer needs in order to bring as much value as possible to the firm and the clients it serves. Consequently, though we will briefly describe some algorithms and explain how to implement them, we will focus on the opportunities offered as well as the drawbacks and pitfalls to avoid in order to make the most out of these methodologies.