TY - GEN
T1 - Recommending insurance riders
AU - Rokach, Lior
AU - Shani, Guy
AU - Shapira, Bracha
AU - Chapnik, Eyal
AU - Siboni, Gali
PY - 2013/5/27
Y1 - 2013/5/27
N2 - Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of rec-ommender systems to the task of matching riders to clients. This task is difficult because of the variety of possible riders, as well as the poor knowledge of the client over these riders. We focus on call centers where the agent also has limited knowledge and expertise. For such agents, discovering appropriate riders for the current client is very difficult, and automated tools that suggest such riders can play an important role in the agent-client dialogue, and may influence considerably the outcome of the interaction. This paper presents and discusses in detail the problem of recommending insurance riders to clients in call centers, comparing it to other, classic, recommendation system applications. In addition, we present an analysis of customer purchase behavior, showing that simple item-item recommendation algorithms provide good recommendations for riders given a base policy.
AB - Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of rec-ommender systems to the task of matching riders to clients. This task is difficult because of the variety of possible riders, as well as the poor knowledge of the client over these riders. We focus on call centers where the agent also has limited knowledge and expertise. For such agents, discovering appropriate riders for the current client is very difficult, and automated tools that suggest such riders can play an important role in the agent-client dialogue, and may influence considerably the outcome of the interaction. This paper presents and discusses in detail the problem of recommending insurance riders to clients in call centers, comparing it to other, classic, recommendation system applications. In addition, we present an analysis of customer purchase behavior, showing that simple item-item recommendation algorithms provide good recommendations for riders given a base policy.
UR - http://www.scopus.com/inward/record.url?scp=84877977606&partnerID=8YFLogxK
U2 - 10.1145/2480362.2480417
DO - 10.1145/2480362.2480417
M3 - Conference contribution
AN - SCOPUS:84877977606
SN - 9781450316569
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 253
EP - 260
BT - 28th Annual ACM Symposium on Applied Computing, SAC 2013
T2 - 28th Annual ACM Symposium on Applied Computing, SAC 2013
Y2 - 18 March 2013 through 22 March 2013
ER -