Identifying actionable customer behavior through advanced analysis of bank transaction data

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.