@inproceedings{PAR00024776,
  title = {{I}dentifying actionable customer behavior through advanced analysis of bank transaction data},
  author = {{N}esvijevskaia, {A}. and {M}artenot, {V}. and {F}ogel, {P}. and {Z}ucker, {J}ean-{D}aniel},
  editor = {},
  language = {{ENG}},
  abstract = {{A}rtificial {I}ntelligence has opened new doors for customer relationship personalization by capturing life events to tailor front and back-office interactions. {I}ndividual bank account data are particularly rich in information on these life events, but few banks have gone beyond its basic use. {I}n 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. {T}he approach includes unsupervised methods that limit upstream feature engineering and are based on a global modeling of a customer's journey through sequence objects.},
  keywords = {{C}ustomer {B}ehavior ; {B}ig {D}ata ; {L}ife events detection ; {U}nsupervised {M}achine ; {L}earning ; {B}ank transactions data},
  numero = {},
  pages = {1551--1552},
  booktitle = {},
  year = {2021},
  DOI = {10.1109/csci54926.2021.00302},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00024776},
}