Fiche du document
15 décembre 2021
- handle: 10670/1.q9xpjp
- hal: hal-03761606
- doi: 10.1109/CSCI54926.2021.00302
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1109/CSCI54926.2021.00302
Mots-clés
Industries Scientific computing Banking Behavioral sciences Artificial intelligence Computational intelligenceSujets proches
Natural science Sciences Science of science Life--Philosophy Intellectronics AI (Artificial intelligence) Artificial thinking Electronic brains Intelligent machines Intelligence, Artificial Machine intelligence Thinking, Artificial IACiter ce document
Anna Nesvijevskaia et al., « Identifying actionable customer behavior through advanced analysis of bank transaction data », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1109/CSCI54926.2021.00302
Métriques
Partage / Export
Résumé
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.