Dynamic Customer Embeddings for Financial Service Applications

TL;DR

This paper introduces Dynamic Customer Embeddings (DCE), a novel framework that uses digital activity and financial context to create dense customer representations, improving prediction tasks in financial services.

Contribution

The paper presents a new dynamic embedding method that incorporates session sequences and financial features for better customer modeling in FS applications.

Findings

DCE improved prediction accuracy for customer intent.

DCE increased detection of potentially fraudulent sessions.

DCE reduced call center follow-up rates.

Abstract

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.