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Edwin Setiawan Nugraha
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Journal of Actuarial, Finance, and Risk Managment
Published by President University
ISSN : -     EISSN : 28303938     DOI : -
Core Subject : Economy, Education,
Economics, Econometrics & Finance Mathematics
This journal aims to provide high quality articles covering any and all aspects of the most recent and significant developments in the actuarial, financial, and risk management.
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 30 Documents
 1   2   3 
Analysis of Premium Reserve Using Zillmer Method and Canadian Method for Endowment Joint Life Insurance Yuhza Al Ghifari; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 2, No 1 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i1.4551

Abstract

Several life insurance companies are unable to compensate policyholder prompting financial losses, the situation can be foreseen if the insurance company has a properly established and calculated reserve value. Endowment life insurance is one types of life insurance. Life insurance provides protection for one person (single life) or two or more people (multiple life). According to the insured death status, there are two terminologies used in multiple life insurance: joint life and last survivor. The Zillmer Method and Canadian Method used in this study for 3 age cases for a couple of husband and wife whereas a husband is older than wife, a husband has the same age as wife, and a husband is younger than wife to determine the amount of reserves adjusted for endowment joint life insurance. Researchers first determine the benefits, then calculate the annuity, and finally calculate the annual premium in order to compute reserves. The Zillmer Method premium reserve value is minus in the beginning year to cover cost for the company, meanwhile Canadian Method is not. According to the result of this study, the case that the age of wife is same as the husband have lesser reserve than any cases which represent this is beneficial for the company to cover several costs for the policy in the beginning of period. Based on data analysis, the period of the insurance contract and the age of the insured define the reserve value. The older the insurance participant, the lesser the value of reserve.
Forecasting the Weekly Stock Price of PT. OCBC NISP Tbk. using Auto Regressive Integrated Moving Average Elisabeth Gloria Manurung; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4812

Abstract

Stocks are widely used in financial markets and can be an option for companies seeking to raise funds. Additionally, investors often opt for stocks as an investment due to their potential for providing high returns. To aid investors in making informed decisions when buying and selling stocks and mitigating risks, professionals have developed different theories and analyses to forecast stock prices. Auto Regressive Integrated Moving Average (ARIMA) (p,d,q) technical analysis will be used in this study to predict the weekly stock price of PT Bank OCBC NISP Tbk (NISP.JK) for 7 weeks from Jan 7, 2022 to February 18, 2022. In this study, historical weekly stock price data for PT. Bank OCBC NISP Tbk (NISP.JK) from 1 January 2021, to 31 December 2021 was collected from Yahoo Finance website to create a forecast. The researches got 12 different ARIMA models, then the researcher determined that the second model (ARIMA (2,2,1) was the most effective. This model was chosen because it has second lowest AIC value and lowest MSE, RMSE, and MAE.
Online Shopping Website Analysis for Marketing Strategy Using Clickstream Data and Extra Trees Classifier Algorithm Diah Prastiwi
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3676

Abstract

On an online shopping website, the platform may provide a service to the shop owners by suggesting which items to promote. One possible consideration is price. If an item is priced more expensively than the average price of other items in the same category, then the item should be advertised more intensely, or repriced. Due to the quickly growing number of products and categories, calculating the average price in real time can be difficult or slow. Alternatively, one may employ machine learning algorithms. In this study, we use Extra Trees Classifier on clickstream data, which is user activity report. We demonstrate the algorithm on the clickstream data of a an online shopping website for pregnant women, retrieved from UCI Machine Learning Repository Dataset. The data has 14 attributes and 165474 entries. The model is trained on 75% of the data, and tested on the remaining 25%, with an observed accuracy of 99 %.
Forecasting of YG Entertainment Stock Prices February 2022-August 2022 Using Arima Model Novia Galuh Ramadhanty; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3972

Abstract

The stock price in investing is the main factor in determining whether an investor will invest there. With stock price prediction research, investors have an idea of whether to invest in the company. YG Entertainment is a public company in the entertainment sector with many artists and entertainment projects that have fluctuating prices. With the ARIMA (Autoregressive Integrated Moving Average) forecasting method, we can predict YG Entertainment's stock price. In this article, YG Entertainment's prediction using the ARIMA model results in a MAPE error rate of 11% with the best model being ARIMA (0,1,0). The error of the model are 33160 x103 MSE, 5758.543 RMSE, and 4366.446 MAE. This forecast will produce good output as consideration for investor who interesting buy YG Entertainment stock price
Estimation of Premium Reserves for Last Survivor Endowment Insurance Using the New Jersey Method Bella Dwi Ananda; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 2, No 1 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i1.4564

Abstract

There are a few cases of life insurance firms going bankrupt due to mistakes when estimating premium reserves, causing companies unable to pay compensation to policyholders. This is caused when the number of claims submitted by the insured that must be paid exceeds the number of claims previously estimated. Situations like this can be anticipated if the insurance firm has a properly prepared and calculated reserve value. There are various types of life insurance, one of which is endowment life insurance. The purpose of this study is to calculate the amount of reserves adjusted for last survivor endowment life insurance using the New Jersey method. To compute reserves, first, researchers determine the benefits, then the annuity, and finally the annual premium. In the first year, the premium reserve value under the New Jersey method is zero. The New Jersey method begins premium reserves in the second year, for t years, with  where  reflecting the duration of the insurer's contract. Based on the data analysis performed, the value of the New Jersey reserves for the two insureds is determined by the length of the insurance contract and the age of the insured at the time of insurance. The value of reserves is seen based on the initial age of a person when starting insurance, the older the initial age of the insurance participant, the greater the amount of reserves obtained by the company. This research is expected can be a reference and help insurance field to estimate premium reserves.
Comparison Between Machine Learning Regression Modelling to Predict Individual Premium Price Srava Chrisdes Antoro; Elisabeth Gloria Manurung; essykapna Randalline; Maria Yus Trinity Irsan
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4807

Abstract

Machine Learning (ML) applications in healthcare aim to simplify people's lives by swiftly predicting and diagnosing diseases, outpacing the capabilities of most medical experts. A direct connection is established when technology, particularly digital health insurance, is employed to minimize the gap between insurance providers and policyholders. This has significantly transformed the way insurers create health insurance policies and has led to faster service delivery for consumers. Machine learning is utilized by insurance companies to offer clients precise, prompt, and efficient health insurance coverage. In this study, a regression method was trained and assessed to which one gets the bigger accuracy to forecast premium prices. The researchers accurately predicted the premium prices individuals incur based on various factors, such as age, diabetes, blood pressure issues, height, and weight. The experimental outcomes revealed the best method to predict is the KNN method in the data set that was used in the analysis, with an impressive accuracy of 87.73%. In comparison, the Random Forest is 87%, and the Boosting is 87.19% and the authors analyzed the model's performance using key metrics to assess its effectiveness. 
Forecasting of Retirement Insurance Filled via Internet by ARIMA Models Lovena Louisa; Rifky Fauzi; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3672

Abstract

Pension fund insurance is critical for everyone because it can guarantee a good life during retirement because retirement is a period when someone no longer gets a steady income. Technological advances make it easier for retirement insurance applications. By using ARIMA Models, we can predict the number of internet users who apply for retirement insurance via the internet, using the monthly data of the Social Security Administration from January 2008 to October 2020. The data used has a steady increasing trend with some seasonal components, so it needs to be removed first. ARIMA models use the assumption that the data is stationary, so the data must be tested using the ADF test command in R. After seeing the plotting of ACF and PACF, 9 ARIMA models are formed. ARIMA model is selected based on the smallest AIC. By using 95% confidence it can be concluded that ARIMA (9,1,9) is the best model for forecasting.
Prediction of Loan Status Using Logistics Regression Model and Naïve Bayes Classifier Christabell Christabell; Edwin Setiawan Nugraha; Karunia Eka Lestari
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3968

Abstract

Conducting an evaluation process of prospective debtors is important for creditors to reduce the risk of default. For this reason, the research aims to construct a model that can determine whether a prospective applicant's credit application is recommended to be accepted or rejected by using the method of logistic regression and naïve Bayes classifier. We used a dataset of gender, married, dependent, education, self-employed, applicant income, co-applicant income, loan amount, loan amount term, credit history, and property area as predictor variables and loan status as a response variable. The results show that the performance measures, including accuracy, precision, recall, and F1 score of the logistics regression method, are 85.9%, 83.82%, 100%, and 91.2%, while the naïve Bayes classifier is 84.62%, 83.58%, 98.2%, and 90.32%. Since the performance measures of logistic regression are bigger than naïve Bayes classifier, it suggests that logistic regression is better than naïve Bayes classifier
Forecasting the Monthly Stock Price per Share of Taiwan Semiconductor Manufacturing Company Limited (TSM) using ARIMA Box-Jenkins Method Gabriella Maria Singgih; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 2, No 1 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i1.4560

Abstract

Taiwan Semiconductor Manufacturing Company Limited is a Taiwanese multinational semiconductor contract manufacturing and design company. People can buy Stocks from Taiwan Semiconductor Manufacturing Company Limited. Stocks are one of the attractive investment instruments for companies and individuals. There are some theories and analyses to predict stock prices to help investors make wiser decisions when buying and selling stock portfolios. In this study, researchers will use ARIMA(p,d,q) technical analysis to predict the stock price of Taiwan Semiconductor Manufacturing Company Limited for the next 5 months from January 1, 2005 to May 1, 2005. For this forecasting, researchers used Taiwan Semiconductor Manufacturing Company Limited historical stock price data from January 1, 1998 to December 31, 2004 that was obtained from the Yahoo Finance website. Based on the test results of 8 ARIMA models, the best model that researchers got is model 2 ARIMA (2,1,1) with the equation Yt = 0.0759Yt-1 + 0.2706Yt-2 + et - 0.198et-1. This model is considered to be the best because it has the smallest MSE Value, which is 0.1076018; the smallest RMSE value, which is 0.0301156; the smallest MAE value, which is 0,2495926; and the smallest MAPE value, which is 3.0116%. This study shows that the stock price is predicted to rise for the next 5 months from January 1, 2005 to May 1, 2005.
Annual Premium Calculation On Single Life Insurance using Gompertz Mortality Assumptions Michelle Novia; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Premium calculation is one of the important aspects to insurance companies. Given the importance of premiums in insurance contracts for insurance companies, determining the price of the premium must also be appropriate. Careless determination of the premium price can cause the insurance company to fail to bear the risk that the company has. There are several ways to determine premium payments. In this research the premium calculation will be computed using Gompertz mortality assumptions which will be applied to the annual premium calculation of whole life single life insurance of man and woman. The benefit assumed, interest rate, Insurer age, Gompertz parameter and several actuarial notations such as life annuity-due and net single premium is needed in the premium calculation using Gompertz mortality assumptions. This research uses the data of Indonesian Mortality table (TMI IV) and the Linear Least Squares (LLS) method to find the Gompertz parameter which then be used to find the life annuity-due that will be needed to compute the premium calculation of Gompertz assumptions. Based on the calculation performed in this research, the value of the premium using Gompertz assumptions is influenced by parameters on the Gompertz assumptions, the interest rate used, and the Insured age.

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