DETERMINATION OF CREDIT INSURANCE PRODUCT PREMIUMS BASED ON LOAN VALUE GROUPING

• Main Author: Agustina, Ellys
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/83911
• Institution: Institut Teknologi Bandung

The disbursement of credit in Indonesia has reached IDR 7,090 trillion according to OJK data; however, the Non-Performing Loan (NPL) rate, which ranges from 2-3%, indicates a significant risk of default. Credit insurance becomes crucial for mitigating potential financial losses and maintaining economic stability. This study models the losses due to defaults in a credit portfolio and determines the appropriate insurance premium. Credit risk is modeled through default events using an indicator function, while the frequency of default events is modeled using the Probability Generating Function (PGF). The credit portfolio is divided into several sub-portfolios through exposure discretization, and losses are expressed in the form of PGF. The loss probability function is derived from the PGF of event frequency, simplified with the Panjer algorithm. The expected loss value is calculated as the first derivative of the PGF loss distribution, while unexpected loss is determined using Value at Risk (VaR) and Expected Shortfall (ES). Reserve from additional initial capital calculated from the difference between expected loss and unexpected loss based on VaR needs to be prepared in the amount of Rp 2,210,000,000.00. The insurance premium model is developed based on the expectation principle and tested through simulation, showing that this approach can be easily applied and yields logical results. The Panjer algorithm has proven to enhance computational efficiency, especially in large portfolios. Results indicate that a larger unit ???? increases the expected loss, benefiting insurance companies by collecting larger premiums. However, a smaller ???? value is fairer for obligors, as premiums are paid according to the risk faced. Premiums are calculated fairly, with the lowest being IDR 5,563,504.40 and the highest being IDR 36,653,676.04. This provides practical guidance for insurance companies in managing credit risk and setting appropriate premiums.