Implementation of predictive data mining technique to predict Claim Cost of Risk Items under Motor Class of Business: The Case of Nigeria Insurance Company 

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Implementation of predictive data mining technique to predict Claim Cost of Risk Items under Motor Class of Business: The Case of Nigeria Insurance Company

The purpose of this study was to identify risk items with high claim ratios in order to take appropriate measures during the underwriting process to save profit-making risk items under the motor class of business. Even if the motor class of business takes a big portion of the premium collection in the Nigerian insurance industry, most insurance companies indicate the motor class of business as a loss-making line of business in their annual report. The main cause for this loss contribution is there are some risk items with high claim ratio which consumes a lot of the premium from the pool. Identifying those risk items from profit-making risk items helps a lot for an insurance company to maximize its profit. To tackle the problem of high claim cost in a motor class of business, predictive data mining techniques have been employed using SVM, Naïve Bayes, and Logistic Regression predictive models. The dataset used for the experiment in this study was collected from a Nigeria insurance company specifically from underwriting and claim data tables of motor class of business. After cleaning irregularities and incomplete data in the dataset, a total of 52,831 records have been used to train the models in the ratio of 80:20. Among the used predictive models Naïve Bayes model outperformed the other two scoring 97.56% of accuracy and 98.7% precision. The challenging part of this study is the lack of uniformity in conducting the underwriting process. The underwriter may use either configured rate premium calculation which is similar throughout the company or a flat rate which is specific to a branch and customer. This creates a lack of uniformity throughout the company in terms of premium calculation. On the other hand, most of the records under configured premium calculation rate are complete and the values of attributes selected for this study are mandatory for the underwriter to be captured during the underwriting process. Since predictive data mining techniques are aimed to identify the pattern of records in the dataset, only those risk items that have got insurance coverage with configured premium calculation rates in Nigeria insurance are included in this study. The predictive modes have been checked by the new risk item as a prototype which is different from the testing date and the outcome confirms the models are well-trained and work correctly.

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