Discovering Temporal Patterns from Insurance Interaction Data.

Maleeha Qazi, Srinivas Tunuguntla, Peng Lee, Teja Kanchinadam, Glenn Fung, Neeraj Arora

Abstract: In the insurance industry, timely and effective interaction with customers are at the core of everyday operations and processes that are key for a satisfactory customer experience. These interactions often result in sequences of data derived from events that occur over time. Such recurrent patterns can provide valuable information that can be used in a variety of ways to improve customer related work-flows. In this paper we demonstrate the application of a recently proposed algorithm to uncover such time patterns that takes into account the time between events to form such patterns. We use temporal customer data generated from two different use-cases (satisfaction and fraud) to show that this algorithm successfully detects patterns that occur in the insurance context.