2025

CALCULATION OF NET PREMIUM ON BONUS MALUS SYSTEM FOR MOTOR VEHICLE INSURANCE

2026-02-11T02:50:36Z

CALCULATION OF NET PREMIUM ON BONUS MALUS SYSTEM FOR MOTOR VEHICLE INSURANCE Alfarisi, Muhammad This research aims to develop a motor vehicle insurance premium calculation model using the Bonus Malus system (BMS) that adjusts premiums based on the policyholder's claim history. This system provides a bonus (premium decrease) if there is no claim and malus (premium increase) if there is a claim. The research modeled the frequency of claims with a Poisson-Gamma Lindley (GaL) distribution and the severity of claims with a Lognormal-Gamma distribution, using a Bayesian approach to produce fairer and more accurate premiums. The data used came from 2004-2005 motor vehicle insurance policies at Macquarie University, Australia, with a total of 67,856 policies and 4,624 claims. Distribution fit tests confirmed that the claim frequency data fit the Poisson-GaL distribution (Chi-Square test statistic: 1.150 < 5.991) and the claim severity data fit the Lognormal-Gamma distribution (Anderson-Darling test statistic: 0.399 < 2.492). Parameter estimation using Maximum Likelihood Estimation (MLE) resulted in parameter values for Poisson-GaL (�㔃 = 18.553; Ā = 1.460) and Lognormal-Gamma (Ā = 6.956; Ā = 9.996; ÿ = 10.293). The results showed that the initial premium without a claim was $ 127.77. If the policyholder makes a claim in the first year, the premium increases to $ 225.37, while without a claim, the premium drops to $ 120.17. This system promotes premium fairness based on individual risk profiles, optimizes the financial stability of insurance companies, and motivates responsible behavior. Recommendations for future research include considering external factors such as government policies and applying the model to data from different regions or periods for broader validation.

FORECASTING THE GROUP HEALTH INSURANCE CLAIM SIZE FOR OUTPATIENT BENEFIT USING ARIMA METHOD

2026-02-11T02:47:43Z

FORECASTING THE GROUP HEALTH INSURANCE CLAIM SIZE FOR OUTPATIENT BENEFIT USING ARIMA METHOD Alkatraz, Salwa Fayza Group health insurance is a health insurance product that provides health protection to a group of individuals that covers their medical expenses and related cost if they experience illness or accident that offers benefits such as lower premiums and easier risk selection. As an insurer, an insurance company needs to prepare for the size of claims that will occur in the next period in order to minimize the misfortunes caused by uncertain or inflated claims. This study predicts the number of group health insurance claims using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method uses historical data to forecast future values by identifying patterns and trends. This research is based on the need for PT. ABC to forecast future claims based on claims data from September 2022 to August 2023, which shows random fluctuations. With the ARIMA (3,1,0) model, this study successfully predicted the number of group health insurance claims for outpatient care. The model was selected based on Akaike Information Criterion (AIC) and validated using Shapiro-Wilk and Ljung-Box tests, which confirmed the normality and absence of autocorrelation in the residuals. The ARIMA (3,1,0) model was selected because it was the only model that met the statistical test requirements. This study forecasts claim amounts for the next five weeks, from August 26th, 2023, to September 23rd, 2023. The results show an MAE and RMSE of 970,418,438, an MSE of 1.36 × 1018, and a MAPE of 4.05%, indicating a high level of accuracy.

ANALYSIS OF FINANCIAL DISTRESS AND FINANCIAL REPORTING FRAUDULENT TO PREVENT PAYMENT FAILURE: CASE STUDY OF INSURANCE INDUSTRY

2026-02-11T02:45:59Z

ANALYSIS OF FINANCIAL DISTRESS AND FINANCIAL REPORTING FRAUDULENT TO PREVENT PAYMENT FAILURE: CASE STUDY OF INSURANCE INDUSTRY Nabila, Haura Nizar According to CNBC, in recent years several Indonesian insurance companies have faced problems due to their failure to pay claims due to financial distress and significant increase of financial reporting fraud cases that occurred that led to a loss of public trust. Understanding a company's financial condition is very important for customers and investors before buying a policy and shares of the company. Therefore, it can be a reference before placing their money in a company to be able to know how much risk they might suffer in the future. Insurance companies are businesses that are vulnerable to bankruptcy because the nature of the business is influenced by public trust. This research aims to determine if the companies are experiencing financial distress and indicated to commit financial reporting fraud as a result of pressure or encouragement to manipulate the financial reporting and hide the truth about company performance. The sample used in this research was financial reporting of 18 public insurance companies registered on IDX from 2020-2023. Altman Z-Score is used to predict financial distress and Beneish M-Score is used to detect financial report fraud. The result of the research calculations is that by the period 2020-2023 there were 2 companies that were predicted to be experiencing financial distress and 5 companies in the grey zone. Moreover, there were 4 companies that are predicted to be experiencing financial distress and detected to commit financial reporting fraud. The result of this research might be helpful for customers and investors in making decisions.

COMPARISON OF MOMENT AND MAXIMUM LIKELIHOOD METHODS IN ESTIMATING PARAMETER GAMMA DISTRIBUTION

2026-02-11T02:45:39Z

COMPARISON OF MOMENT AND MAXIMUM LIKELIHOOD METHODS IN ESTIMATING PARAMETER GAMMA DISTRIBUTION Wibisono, Pelangi Cinta Kirana Claim prediction plays a crucial role in the insurance industry, enabling companies to design suitable insurance policies for potential policyholders. One approach to predicting claims involves using the parameters of the gamma distribution. Several methods can be applied, including Maximum Likelihood Estimation (MLE), the Method of Moments (MoM), and the Bayesian method. This study focuses on comparing the MoM and MLE methods to determine the most effective approach for predicting insurance claim frequency using Google Collab. The analysis is based on secondary data obtained from Kaggle. The MoM estimates parameters by equating k sample moments with the corresponding k population moments, while MLE works by maximizing the likelihood function. The findings indicate that MLE produces a lower error rate of 0.424%, compared to 0.6845% for MoM. This suggests that Maximum Likelihood Estimation (MLE) provides higher accuracy in predicting insurance claim frequency.