Nesvijevskaia A. auteur aut IRD Martenot V. auteur aut IRD Fogel P. auteur aut IRD Zucker Jean-Daniel auteur aut IRD International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas (USA), 2021/12/15-17 text conferencePaper eng Artificial Intelligence has opened new doors for customer relationship personalization by capturing life events to tailor front and back-office interactions. Individual bank account data are particularly rich in information on these life events, but few banks have gone beyond its basic use. In this paper, we describe an innovative and original methodological framework to give meaning to bank transactions and make them actionable under operational and regulatory constraints. The approach includes unsupervised methods that limit upstream feature engineering and are based on a global modeling of a customer's journey through sequence objects. specialized Customer Behavior Big Data Life events detection Unsupervised Machine Learning Bank transactions data 122 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas (USA), 2021/12/15-17 1551-1552 2021 https://www.documentation.ird.fr/hor/PAR00024776 10.1109/csci54926.2021.00302 https://www.documentation.ird.fr/hor/PAR00024776 IRD - Base Horizon / Pleins textes 2022-09-28 2025-02-24 PAR00024776 fre