Dynamic Customer Embeddings for Financial Service Applications

Autor:	Chitsazan, Nima, Sharpe, Samuel, Katariya, Dwipam, Cheng, Qianyu, Rajasethupathy, Karthik
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICML Workshop on Representation Learning for Finance and E-Commerce Applications
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.11880 Zobrazit plný text záznamu View this record from Arxiv