INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.9, No.3, Mar. 2017

A Knowledge-Based System for Life Insurance Underwriting

Full Text (PDF, 641KB), PP.40-49


Views:114   Downloads:10

Author(s)

Mutai K. Joram, Bii K. Harrison, Kiplang'at N. Joseph

Index Terms

Life insurance;underwriting;Knowledge management;Knowledge engineering;Knowledge-based system

Abstract

The purpose of this work is to enhance the life insurance underwriting process by building a knowledge-based system for life insurance underwriting. The knowledge-based system would be useful for organizations, which want to serve their clients better, promote expertise capture, retention, and reuse in the organization. The paper identifies the main input factors and output decisions that life insurance practitioners considered and made on a daily basis. Life underwriting knowledge was extracted through interviews in a leading insurance company in Kenya. The knowledge is incorporated into a knowledge-based system prototype designed and implemented, built to demonstrate the potential of this technology in life insurance industry. Unified modelling language and visual prolog language was used in the design and development of the prototype respectively. The system's knowledge base was populated with sample knowledge obtained from the life insurance company and results were generated to illustrate how the system is expected to function.

Cite This Paper

Mutai K. Joram, Bii K. Harrison, Kiplang'at N. Joseph,"A Knowledge-Based System for Life Insurance Underwriting", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.3, pp.40-49, 2017. DOI: 10.5815/ijitcs.2017.03.05

Reference

[1]Alavi, M. & leidner, D. E. (2001). Knowledge Management and Knowledge Management Systems, MIS Quarterly, 25 (1), 107-136.

[2]Reilly Frank, k. & Brown Keith, C. (2011). Investment Analysis and portfolio Management (10th ed.). USA: Cengage learning.

[3]Turner, D.E. & Turner P.H. (1994). Business Management Studies. Malaysia: McMillan.

[4]Yeates D. & Cadle James (2007). Project Management for Information Systems (5th ed.). UK: Prentice Hall.

[5]Saleemi, N. A (2007). Storekeeping and stock control Simplified. Nairobi: Saleemi Publications Limited.

[6]Negnevitsky, M. (2011). Artificial Intelligence: A guide to Intelligent Systems. (3rd ed.). Canada: Pearson Education Limited.

[7]Turban E. (1992), Expert systems and Applied Artificial Intelligence, New York: Macmillan 

[8]Wijnhoven, F. (2003). Operational Knowledge Management: Identification of Knowledge objects, operation Methods and Goals and means for the Support Fuction”. The Journal of operational Research Society, 54 (2), 196-203

[9]Walliman, N. (2011). Your Research Project (3rd ed.) London: SAGE Publications Ltd.

[10]Creswell, J. (2013). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). USA: Sage Publications

[11]Sommerville, I. (2010). Software Engineering (9th ed.). U.K. Addison Wesley.

[12]Neale, M.  (2004).  Modelling Expertise for knowledge Development. The Journal of the operational Research Society, 41(5), 447-459.

[13]Holt J. & Perry, S. (2010). Modelling Enterprise Architectures (Let Professional Application Series). London: Institution of Engineering and Technology

[14]Hakansson, A. (2001). UML as an approach to Modelling Knowledge in Rule-based Systems. In proceedings of the 21st  SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Peterhouse College, Cambridge, UK.

[15]Kendal and Kendal (2008). Systems Analysis and Design (7th ed.). India: Prentice Hall Dorling Kindersly pvt ltd.