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2019 AAAI AAAI 2019

Discovering Temporal Patterns from Insurance Interaction Data

Abstract

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.

🚀 Conference Pioneer — AAAI 2019
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — sequence mining
🐣 Hot Topic Early Bird — time series analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Optimization & Theory > Stochastic Processes Data Science & Analytics > Methods > Time Series Analysis Data Science & Analytics > Applications > Clustering Data Science & Analytics > Applications > Information Retrieval Machine Learning > Learning Types > Sequence Modeling

Keywords

time series analysis time series clustering fraud detection temporal pattern pattern mining pattern discovery sequence mining customer interaction recurrent pattern sequence datum insurance datum
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