Discovering Temporal Patterns from Insurance Interaction Data

Authors

  • Maleeha Qazi American Family Insurance
  • Srinivas Tunuguntla University of Wisconsin - Madison
  • Peng Lee American Family Insurance
  • Teja Kanchinadam American Family Insurance
  • Glenn Fung American Family Insurance
  • Neeraj Arora University of Wisconsin - Madison

DOI:

https://doi.org/10.1609/aaai.v33i01.33019573

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.

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Published

2019-07-17

How to Cite

Qazi, M., Tunuguntla, S., Lee, P., Kanchinadam, T., Fung, G., & Arora, N. (2019). Discovering Temporal Patterns from Insurance Interaction Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9573-9580. https://doi.org/10.1609/aaai.v33i01.33019573

Issue

Section

IAAI Technical Track: Emerging Papers