| Peer-Reviewed

Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining

Received: 25 June 2018     Accepted: 5 December 2018     Published: 17 January 2019
Views:       Downloads:
Abstract

Risk management has long been a topic worth pursuing, and indeed several industries are based on its successful applications, insurance companies and banks being the most notable. Data Mining (DM) - is one of the most effective alternatives to extract knowledge from the great volume of data, discovering hidden relationships, patterns and generating rules to predict and correlate data, that can help the institutions in faster decision-making or, even reach a bigger degree of confidence. This research was conducted in a form of case study in the Ethiopian Insurance Corporation (EIC) at its main branch located at Legehar- Addis Ababa. The general objective of the study is to examine the potential of data mining tools and techniques in developing models that could help in the effort of Risk level pattern analysis with the aim of supporting insurance risk assessment activities at EIC. In this research two data mining technique which are decision tree and neural network. The best decision tree model, which is selected as a working model among the numerous models generated during the training phase, was able to correctly classify 75% percent of the 3100 policies in the validation data set. 96% of low-risk policies were correctly classified. Significant number of misclassification was observed on high risk level. The output of these experiments indicated that the classification task of records using the Risk level, both decision tree and neural network have performed with significant error. Decision tree has shown an accuracy rate of 75 percent while neural networks classified 58% records correctly. The overall performance of decision tree was better in classifying values than neural network.

Published in Software Engineering (Volume 6, Issue 4)
DOI 10.11648/j.se.20180604.13
Page(s) 121-127
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Data Mining, Risk Assessment, Decision Tree, Neural Network, Ethiopia

References
[1] Kennet, Y. a. (2001). Operational Risk Management: Apractical approach for data analysis.
[2] Apet, e. a. (1998). Insurance Risk modeling Using Data mining Technology.
[3] Dockrill, M. et al. (2001). Underwriting Management. Study Course 815. London: CII publishing Division.
[4] Cardoso ONP, Machado RTM. Knowledge management using data mining: a case study at the Federal University of Lavras. Rev Public Adm. 2008; 42 (3): 495-528.
[5] Rodrigues RJ. Information systems: the key to evidence-based health practice. Bull World Health Organ. 2000; 78(11): 1344-51.
[6] Stetco, A. X.-J. (2015). "Fuzzy C-means++: fuzzy C-means with effective seeding initialization.” Expert Systems with Applications 42.21, 7541-7548.
[7] Madeira, S. a. (2002). "Comparison of target selection methods in direct marketing." European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems.
[8] Ansari, A. a. (2016). "Customer clustering using a combination of fuzzy c-means and genetic algorithms.” International Journal of Business and Management 11.7, 59.
[9] Qu, Y. e. (2017). "Associated multi-label fuzzy-rough feature selection”. Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). 2017 Joint 17th World Congress of International. IEEE.
[10] Finkelstein, A. a. (2014). "Testing for asymmetric information using “unused observables” in insurance markets: Evidence from the UK annuity market.”. Journal of Risk and Insurance 81.4, 709- 734.
[11] Rahman, M. S. (2017). "Analyzing Life Insurance Data with Different Classification Techniques for Customers’ Behavior Analysis.”. Advanced Topics in Intelligent Information and Database Systems. Springer International Publishing, 15-25.
[12] Kang, S. J. (2018). "Feature selection for continuous aggregates response and its application to auto insurance data.” Expert Systems with Applications 93, 104-117.
[13] Berry, M. a. and Linoff, G. (2000). Mastering Data mining: the art and science of Customer Relationship Management. New York: John Wiley & Sons, inc.
[14] Rokach, Lior; Maimon, O. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc.
[15] Russell, S. & Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, London (2003).
Cite This Article
  • APA Style

    Sisay Wuyu, Patrick Cerna. (2019). Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining. Software Engineering, 6(4), 121-127. https://doi.org/10.11648/j.se.20180604.13

    Copy | Download

    ACS Style

    Sisay Wuyu; Patrick Cerna. Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining. Softw. Eng. 2019, 6(4), 121-127. doi: 10.11648/j.se.20180604.13

    Copy | Download

    AMA Style

    Sisay Wuyu, Patrick Cerna. Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining. Softw Eng. 2019;6(4):121-127. doi: 10.11648/j.se.20180604.13

    Copy | Download

  • @article{10.11648/j.se.20180604.13,
      author = {Sisay Wuyu and Patrick Cerna},
      title = {Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining},
      journal = {Software Engineering},
      volume = {6},
      number = {4},
      pages = {121-127},
      doi = {10.11648/j.se.20180604.13},
      url = {https://doi.org/10.11648/j.se.20180604.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20180604.13},
      abstract = {Risk management has long been a topic worth pursuing, and indeed several industries are based on its successful applications, insurance companies and banks being the most notable. Data Mining (DM) - is one of the most effective alternatives to extract knowledge from the great volume of data, discovering hidden relationships, patterns and generating rules to predict and correlate data, that can help the institutions in faster decision-making or, even reach a bigger degree of confidence. This research was conducted in a form of case study in the Ethiopian Insurance Corporation (EIC) at its main branch located at Legehar- Addis Ababa. The general objective of the study is to examine the potential of data mining tools and techniques in developing models that could help in the effort of Risk level pattern analysis with the aim of supporting insurance risk assessment activities at EIC. In this research two data mining technique which are decision tree and neural network. The best decision tree model, which is selected as a working model among the numerous models generated during the training phase, was able to correctly classify 75% percent of the 3100 policies in the validation data set. 96% of low-risk policies were correctly classified. Significant number of misclassification was observed on high risk level. The output of these experiments indicated that the classification task of records using the Risk level, both decision tree and neural network have performed with significant error. Decision tree has shown an accuracy rate of 75 percent while neural networks classified 58% records correctly. The overall performance of decision tree was better in classifying values than neural network.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Risk Assessment Predictive Modelling in Ethiopian Insurance Industry Using Data Mining
    AU  - Sisay Wuyu
    AU  - Patrick Cerna
    Y1  - 2019/01/17
    PY  - 2019
    N1  - https://doi.org/10.11648/j.se.20180604.13
    DO  - 10.11648/j.se.20180604.13
    T2  - Software Engineering
    JF  - Software Engineering
    JO  - Software Engineering
    SP  - 121
    EP  - 127
    PB  - Science Publishing Group
    SN  - 2376-8037
    UR  - https://doi.org/10.11648/j.se.20180604.13
    AB  - Risk management has long been a topic worth pursuing, and indeed several industries are based on its successful applications, insurance companies and banks being the most notable. Data Mining (DM) - is one of the most effective alternatives to extract knowledge from the great volume of data, discovering hidden relationships, patterns and generating rules to predict and correlate data, that can help the institutions in faster decision-making or, even reach a bigger degree of confidence. This research was conducted in a form of case study in the Ethiopian Insurance Corporation (EIC) at its main branch located at Legehar- Addis Ababa. The general objective of the study is to examine the potential of data mining tools and techniques in developing models that could help in the effort of Risk level pattern analysis with the aim of supporting insurance risk assessment activities at EIC. In this research two data mining technique which are decision tree and neural network. The best decision tree model, which is selected as a working model among the numerous models generated during the training phase, was able to correctly classify 75% percent of the 3100 policies in the validation data set. 96% of low-risk policies were correctly classified. Significant number of misclassification was observed on high risk level. The output of these experiments indicated that the classification task of records using the Risk level, both decision tree and neural network have performed with significant error. Decision tree has shown an accuracy rate of 75 percent while neural networks classified 58% records correctly. The overall performance of decision tree was better in classifying values than neural network.
    VL  - 6
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Department of Information Technology, Federal TVET Institute - University, Addis Ababa, Ethiopia

  • Department of Information Technology, Federal TVET Institute - University, Addis Ababa, Ethiopia

  • Sections