<![CDATA[Based on claim submission data from year 2019 to 2021 can be seen that the percentage of claims in one province is much higher than other provinces. During that period, the percentage of claims in that province reached 22% while the highest percentage in other provinces was only 6%. It is suspected that there has been a claim fraud in the province. The fraud allegedly started when customer submits a policy issuance for the elderly insured with a low sum insured so that the premium is also low. The insured's health condition at that time may not be good but it is not explained in the insurance application letter. To increase the sum insured, the policy is usually added with additional coverage. Fraud claim creates big loss for insurance company since the company has to pay the claim that they should not pay. Insurance company need to have a mechanism to avoid the fraud claim. From this research, it is expected to find the best methodology to be able to predict the potential of insurance claim fraud early when customers apply for policy issuance so that additional checks can be carried out for suspected submissions. The initial data available for this research is 14,778 claim records with attributes are : the date of claim submission, policy effective date, sum assured, type of claim, cause of claim, province and fraud. In order to get the best methodology on the accuracy and performance aspect to fulfill the expectation, two methodologies (Naïve Bayes and K-NN) are compared. Both Naive Bayes and K-NN methods are used with a comparison of training data and testing data is 80:20. Several combinations were performed for each of these methods. By using Confusion Matrix and AUC to measure the accuracy and performance of the two methods, it can be concluded that the best one is Naive Bayes with accuracy is 90% and AUC is 0.761. The attributes used are province, sum assured, additional coverage and the insured is the policy holder.]]>
Copyrights © 2022
