Discovering Temporal Patterns from Insurance Interaction Data


  • 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



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.




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.



IAAI Technical Track: Emerging Papers