Predictive Modeling Using Data Mining Techniques in Support of Insurance Risk Assessment
| dc.contributor.advisor | Abebe Ermias (Ato) | |
| dc.contributor.advisor | Meshesha Million | |
| dc.contributor.author | Hintsay Tesfaye | |
| dc.date.accessioned | 2018-11-30T12:14:33Z | |
| dc.date.accessioned | 2023-11-18T12:44:11Z | |
| dc.date.available | 2018-11-30T12:14:33Z | |
| dc.date.available | 2023-11-18T12:44:11Z | |
| dc.date.issued | 2002-06 | |
| dc.description.abstract | One of the important tasks that we have to face in a real world application is the task of classifying particular situation or events as belonging to a certain class. Risk assessment in insurance policies is one example that can be viewed as classification problem. In order to solve these kinds of problems we must build an accurate classifier system or model. Data mining techniques are powerful tools in addressing such problems. This research describes the development of predictive model, which determines the risk exposure of motor insurance policies. Decision tree and neural network were used in developing the model. Since rejections of policy renewal are rare at Nyala Insurance SC. (NISCO), where the research was conducted as a case study, policies were classified into one of the three possible groups (Low, Medium, or High risk) on the basis of annual assessment made by NISCO. Six variables were extracted from the 25 variables used in this study. 940 facts (90% of the working dataset) were used to build both decision tree and neural network models. The remaining 116 (10 %) of the dataset were used to validate the performance of the models. The decision tree model, selected based on the meaningfulness of the rules extracted from it, correctly classified 95.69% of the validation set, and the classification accuracy for low, medium and high risk policies are 98.15%, 94.12%, and 92.86% respectively. The neural network model correctly classified 92.24 % of the validation set, high-risk groups are correctly classified, and low and medium-risk groups are classified with accuracy of 98.15% and 76.47% respectively. Some possible explanations for the relatively low performance of the neural network with medium policies are given. In addition, an interesting pattern was found between the two models that some policies misclassified by decision tree were correctly classified by neural networks, and vice versa. This is a good indication that the hybrid of the two models may result in better performance | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/14763 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Data Mining | en_US |
| dc.title | Predictive Modeling Using Data Mining Techniques in Support of Insurance Risk Assessment | en_US |
| dc.type | Thesis | en_US |