Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1609/aaai.v33i01.33019573 | AAAI |
Field | DocType | Volume |
Time patterns,Insurance industry,Computer science,Customer experience,Artificial intelligence,Machine learning | Conference | 33 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maleeha Qazi | 1 | 22 | 3.68 |
| Srinivas Tunuguntla | 2 | 0 | 0.34 |
| Peng Lee | 3 | 0 | 0.34 |
| Teja Kanchinadam | 4 | 0 | 1.01 |
| Glenn Fung | 5 | 231 | 13.77 |
| Neeraj Arora | 6 | 0 | 1.35 |