research-article

Recommending insurance riders

Authors:

Bracha Shapira,

Gali SiboniAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 253 - 260

https://doi.org/10.1145/2480362.2480417

Published: 18 March 2013 Publication History

Abstract

Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of recommender 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.

References

[1]

J. Anton. The past, present and future of customer access centers. International Journal of Service Industry Management, 11(2):120--130, 2000.

[2]

J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI '98, pages 43--52, 1998.

Digital Library

[3]

S. Debnath. Machine learning based recommendation systems. Master's thesis, Department of Computer Science and Engineering, Indian Institute of Technology, 2008.

[4]

C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, pages 107--144. 2011.

[5]

R. Driskill and J. Riedl. Recommender systems for e-commerce: Challenges and opportunities. Technical Report WS-99-01, AAAI, 1999.

[6]

R. Dumm and R. Hoyt. Insurance distribution channels: markets in transition. Journal of Insurance Regulation, 22(1):27--48, 2003.

[7]

A. Gilman, B. Narayanan, and S. Paul. Mining call center dialog data. Information and Communication Technologies, Data Mining V, 33:317--325, 2004.

[8]

T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89--115, Jan. 2004.

Digital Library

[9]

R. Holman, D.; Batt and U. Holtgrewe. The global call center report: International perspectives on management and employment. Technical Report 13, Research Studies and Reports. Cornell University, 2007.

[10]

J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40:77--87, 1997 1997.

Digital Library

[11]

Y. Koren and R. M. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 145--186. 2011.

[12]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7:76--80, 2003.

Digital Library

[13]

J. Luan, C. Summa, and M. Weiland. Use of data mining to examine an outreach call center's effectiveness and build a predictive model for classifying future marketing targets. Data mining in E-learning, State-of-the-art in Science and Engineering, 2005.

[14]

M. Montaner, B. López, and J. L. de la Rosa. A taxonomy of recommender agents on the internet. Artif. Intell. Rev., 19(4):285--330, 2003.

Digital Library

[15]

D. Padmanabhan and K. Kummamuru. Mining conversational text for procedures with applications in contact centers. Int. J. Doc. Anal. Recognit., 10:227--238, December 2007.

Digital Library

[16]

M. Paprzycki, A. Abraham, R. Guo, and S. Mukkamala. Data mining approach for analyzing call center performance. In Proceedings of the 17th international conference on Innovations in applied artificial intelligence, IEA/AIE'2004, pages 1092--1101, 2004.

Digital Library

[17]

C. Pfeil, T. Posselt, and N. Maschke. Incentives for sales agents after the advent of the internet. Marketing Letters, 19(1):51--63, 2008.

[18]

U. Rabanser and F. Ricci. Recommender systems: Do they have a viable business model in e-tourism? In Information and Communication Technologies in Tourism, pages 160--171, 2005.

[19]

P. Resnick and H. R. Varian. Recommender systems. Commun. ACM, 40(3):56--58, 1997.

Digital Library

[20]

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In WWW '01: Proceedings of the 10th international conference on World Wide Web, pages 285--295, New York, NY, USA, 2001. ACM.

Digital Library

[21]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In EC '00: Proceedings of the 2nd ACM conference on Electronic commerce, pages 158--167, New York, NY, USA, 2000. ACM.

Digital Library

[22]

J. Schafer, J. Konstan, and J. Riedl. E-commerce recommendation applications. Data Mining Knowledge Discovery, 5:115--153, 01/2001 2001.

Digital Library

[23]

H. Takeuchi, L. V. Subramaniam, T. Nasukawa, and S. Roy. Getting insights from the voices of customers: Conversation mining at a contact center. Information Sciences, 179:1584--1591, 2009.

Digital Library

[24]

C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW, pages 22--32, 2005.

Digital Library

Cited By

Rani ATaneja KTaneja H(2025)Insurance_4_YOU: MCDM Equipped User-Friendly Intelligent System for Personalized Life Insurance RecommendationsIntelligent System and Data Analysis10.1007/978-981-97-5200-3_19(269-282)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-981-97-5200-3_19
Chu JThan JJo H(2024)Deep Learning for Cross-Selling Health Insurance Classification2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10475046(453-457)Online publication date: 17-Jan-2024
https://doi.org/10.1109/GECOST60902.2024.10475046
Sidorowicz TPeres PLi Y(2022)A Novel Approach for Cross-Selling Insurance Products Using Positive Unlabelled Learning2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892762(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892762
Show More Cited By

Recommendations

Recommending new movies: even a few ratings are more valuable than metadata
RecSys '09: Proceedings of the third ACM conference on Recommender systems

The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by ...
Semantic content of motorcycle riders’ eye fixations during lane-splitting
Abstract
A naturalistic field observation study is presented, exploring the experienced riders’ eye fixations on specific traffic objects and their relative semantic content during lane-splitting manoeuvre in urban settings. Six experienced motorcyclists ...
Recommending user generated item lists
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

Existing recommender systems mostly focus on recommending individual items which users may be interested in. User-generated item lists on the other hand have become a popular feature in many applications. E.g., Goodreads provides users with an interface ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

SAC '13

Sponsor:

SIGAPP

SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

Acceptance Rates

SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
226
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Rani ATaneja KTaneja H(2025)Insurance_4_YOU: MCDM Equipped User-Friendly Intelligent System for Personalized Life Insurance RecommendationsIntelligent System and Data Analysis10.1007/978-981-97-5200-3_19(269-282)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-981-97-5200-3_19
Chu JThan JJo H(2024)Deep Learning for Cross-Selling Health Insurance Classification2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10475046(453-457)Online publication date: 17-Jan-2024
https://doi.org/10.1109/GECOST60902.2024.10475046
Sidorowicz TPeres PLi Y(2022)A Novel Approach for Cross-Selling Insurance Products Using Positive Unlabelled Learning2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892762(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892762
Albo C(2022)Alternative Data for Configurable and Personalized Commercial Insurance ProductsBig Data and Artificial Intelligence in Digital Finance10.1007/978-3-030-94590-9_18(313-322)Online publication date: 29-Apr-2022
https://doi.org/10.1007/978-3-030-94590-9_18
Kong HYun WJoo WKim JKim KMoon IKim W(2022)Constructing a personalized recommender system for life insurance products with machine‐learning techniquesIntelligent Systems in Accounting, Finance and Management10.1002/isaf.152329:4(242-253)Online publication date: 28-Nov-2022
https://doi.org/10.1002/isaf.1523
Bi YSong LYao MWu ZWang JXiao JHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start UsersProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401426(2211-2220)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401426
Lesage LDeaconu MLejay AMeira JNichil GState R(2020)A recommendation system for car insuranceEuropean Actuarial Journal10.1007/s13385-020-00236-zOnline publication date: 19-Jun-2020
https://doi.org/10.1007/s13385-020-00236-z
Qazi MTollas KKanchinadam TBockhorst JFung G(2020)Designing and deploying insurance recommender systems using machine learningWIREs Data Mining and Knowledge Discovery10.1002/widm.136310:4Online publication date: 2-May-2020
https://doi.org/10.1002/widm.1363
Atauchi PNedel LGalante R(2019)Broker-Insights: An Interactive and Visual Recommendation System for Insurance BrokerageAdvances in Computer Graphics10.1007/978-3-030-22514-8_13(155-166)Online publication date: 12-Jun-2019
https://doi.org/10.1007/978-3-030-22514-8_13

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten