  As financial services (FS) companies have experienced drastic technology
driven changes, the availability of new data streams provides the opportunity
for more comprehensive customer understanding. We propose Dynamic Customer
Embeddings (DCE), a framework that leverages customers' digital activity and a
wide range of financial context to learn dense representations of customers in
the FS industry. Our method examines customer actions and pageviews within a
mobile or web digital session, the sequencing of the sessions themselves, and
snapshots of common financial features across our organization at the time of
login. We test our customer embeddings using real world data in three
prediction problems: 1) the intent of a customer in their next digital session,
2) the probability of a customer calling the call centers after a session, and
3) the probability of a digital session to be fraudulent. DCE showed
performance lift in all three downstream problems.
