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.

本文提出了一种利用动态客户嵌入的方法，通过客户的数字活动和广泛的金融背景，学习 FS 行业客户的密集表示。使用这种方法，作者在三个预测问题上进行了实际测试，并取得了良好的效果，包括客户下一次数字会话的意图、客户在会话后打电话给客服的概率，以及数字会话被认为是欺诈的概率。