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 |
Data Mining, Risk Assessment, Decision Tree, Neural Network, Ethiopia
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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
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
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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}
}
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 -
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