Discovering Temporal Patterns from Insurance Interaction Data

No. 1: AAAI-19, IAAI-19, EAAI-20

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 33

IAAI Technical Track: Emerging Papers

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.

10.1609/aaai.v33i01.33019573