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Edwin Setiawan Nugraha
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INDONESIA
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 20 Documents
 1   2 
Logistic Regression Analysis of Demographic and Vehicle Condition for Purchasing Vehicle Insurance Gabriel Azhar; Muhammad Cahirul Rahman; Rosyid Nur Salam; Fauziah Nur Fahirah Sudding
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.v1i1.3673

Abstract

Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection or reimbursement against losses from an insurance company. Insurance policies are used to hedge against the risk of financial losses, both big and small, that may result from damage to the insured or her property, or from liability for damage or injury caused to a third party. Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue. In this research, we use the secondary data that collected in India in 2020, which analyzes vehicle condition, demographics, and owning a driver’s license on vehicle insurance buying interest. The method used in this research is the Logistic Regression, the response variable is the Response (of buying vehicle insurance interest), and the independent variables are Gender, Driving License, Previously Insured, Vehicle Age, and Vehicle Damage. The result of this research showed that the Previously Insured, Vehicle Age, and Vehicle Damage have a correlation to the Response.
Comparison of Premium Reserves with New Jersey Methods and Full Preliminary Term on Endowment Insurance Filemon Febrian Bintoro; Fauziah Nur Fahirah Sudding
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.v1i2.3969

Abstract

Life insurance companies often have difficulty getting fees at the beginning of the insurance year. It’s noted that there are several life insurers who suffer losses caused by the inability to pay compensation to the insured, because the value of the claim insured submit exceeds the cliam estimated by the insurer. Those conditions can be anticipated if the insurance company has reserve funds. This study aims to find the right reserve value for insurance companies that have been adapted to endowment life insurance using the New Jersey and Full Preliminary Term method. Based on the data analysis carried out, it was concluded that the New Jersey reserve value for male from 1st year to 49th year is greater than that for females. Meanwhile, for Full Preliminary Term reserves, the value of male’s reserves from the 1st year to the 49th year is relatively always greater than that of females. This research could be used as a reference for insurance company to consider the better method in calculating premium reserves based on its policyholder profile
ARIMA Model and Holt Winters Seasonal Smoothing Accuracy for Stock Price Prediction Agus Sofian Eka Hidayat; Putu Darmawan
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.v2i1.4521

Abstract

One way to generate capital is by investments in the capital market, with the expectation that this money will increase in tandem with an issuer's stock price. In the past few decades, technology for communication and information has developed incredibly quickly, followed by the introduction of 5G, which boosts data transfer rates. This study intends to compare the accuracy of SARIMA and HOLTS WINTER SEASONAL SMOOTHING in predicting the price of TLKM. The SARIMA (1, 1, 1) x (0, 1, 1) 52 model outperforms Holt's Winter Seasonal Smoothing with a 3% MAPE in terms of forecasting TLKM stock price data
Application of the Historical Burn Analysis Method in Determining Rainfall Index for Crop Insurance Premium Using Black-Scholes Agus Sofian Eka Hidayat; Agnes Crycencia Sembiring
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.v2i2.4806

Abstract

Indonesia is an agricultural country where rice sector has a high risk of production loss or crop failure. Agricultural Insurance is one of the government programs that helps farmers secure their farms. The aim of this final assignment is to obtain rainfall index to determine crop insurance premiums. The rainfall index is being carried out using Historical Burn Analysis which will produce exit values and trigger values for the index window January 2014 - December 2022 using secondary data from the Bali Climatology Station. In this study, Microsoft Excel 2016 was used as an application tool. The Black-Scholes method is used to calculate the premium in Jembrana Regency. The calculation of the rainfall index using the Historical Burn Analysis formula in determining agricultural insurance premiums using Black-Scholes can be used very well, the result show that the insurance premium value is very high where the lowest premium is IDR 6,654,075 and the highest premium is IDR 6,781,555.
Log Linear Model on Contingency Table to Analyze Relationship between Age, Income, and Health Insurance Ownership Evelyn Priscilla; Jeslyn Prinssesa; Mei Siang Jemima Aurelia; 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.v1i1.3674

Abstract

Health insurance is a type of insurance that is important for everyone to have since it has benefits as protection against health risks that may occur in the future. Unfortunately, most people nowadays do not really want health insurance, especially people who are relatively young and have low incomes. Young people feel that they are still strong and do not get sick easily, while people with low incomes cannot afford to buy insurance because of the high premium prices. Therefore, the relationship between age, income, and insurance ownership (other than BPJS) needs to be known to help insurance companies develop new strategies. In this study, we implemented a Log-Linear model on a contingency table using survey data that we took in Jabodetabek, Bali, and Kalimantan areas. The results showed that the Log-Linear model (OI.OA.IA) was efficient enough to determine the relationship between age, income, and insurance ownership with a 95% confidence level. Homogeneous interactions happened so that there is no relationship between age, income, and insurance ownership, but there were relationships between age and income, age and insurance ownership, and income and insurance ownership. This research is expected to assist insurance companies in determining their target market and developing their marketing.
Forecasting The Number of Aircraft Passengers Arriving Through Soekarno-Hatta Airport Using Arima Model Windi Marnizal Putri; Fauziah Nur Fahirah Sudding
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.v1i2.3970

Abstract

Soekarno-Hatta International Airport is well known as the busiest airport in Indonesia with the number of airplane passengers normally grow from year to year. In 2010, there were more than 43 million passengers, and had increased up to 62.4 million over the year 2011. Risk of overcapacity became an issue. Thus, in the following year, the airport was planned for an expansion.  Predicting the frequency of passengers can be helpful for future planning and to improve airport facilities and policy. This research used Autoregressive Integrated Moving Average (ARIMA) to forecast the number of aircraft passengers. ARIMA (0,1,1) is the most suitable model used with MAPE 110%, the results is 2,405,205 passengers. Actual data and predictive data are not much different
Forecasting PT Bank Central Asia Tbk Stock Price Using ARIMA Model Agna Olivia; 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.v2i1.4563

Abstract

Stocks are one of the most popular financial market instruments. Issuing shares is one of the company's options when deciding to fund the company. On the other hand, stocks are an investment instrument that many investors choose because stocks are able to provide an attractive level of profit. Autoregressive Integrated Moving Average (ARIMA) model is a method used to predict the stock price of PT Bank Central Asia Tbk. This analysis shows that ARIMA (3,2,0) is the best model for forecasting the stock price of PT. Bank Central Asia Tbk because it has the smallest MAPE among the other model which is 14.03%. This forecasting is very useful for the investor as a guideline in the future for making effective and efficient decisions about stocks on PT Bank Central Asia Tbk.
Value at Risk Calculation of Digital Bank Stocks Portfolio in Indonesia Steffany Indra Gunawan; 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 | DOI: 10.33021/jafrm.v2i2.4810

Abstract

Nowadays, stock investment has been increasingly growing in society. In investing activities, there are risks that may be experienced by investors. However, sometimes many investors do not realize how much risk that they might suffer in the future. One way that can be done to measure this risk is to calculate the Value at Risk (VaR). This study aims to calculate the VaR value of digital bank stock portfolio in Indonesia. The calculation of VaR will be done using two methods, include the Historical Simulation and Monte Carlo Simulation method. From the calculation, VaR with Historical Simulation and Monte Carlo sequentially generate results of IDR 6,006,718 and IDR 10,797,904 for 99% confidence level, IDR 4,135,857 and IDR 5,376,949 for 95% confidence level, and IDR 3,219,885 and IDR 3,417,553 for 90% confidence level. Based on the results, it is found that VaR result is directly proportional to the confidence level used. Through the calculation results, it also found that VaR value with the Monte Carlo Simulation method are greater than those with the Historical Simulation method.
ARIMA Model in Predicting Jakarta Composite Index Shafa Luthfia Sari Haerani; 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.v1i1.3675

Abstract

This study discusses stock price modeling using ARIMA model. We apply to model to the Jakarta Composite Index (JCI) as it represents all stock performances listed in Indonesia Stock Exchange. In this study, we propose several ARIMA models based on the daily from June 10th, 2019 until December 6th, 2019. The parameters among the models are estimated by using RStudio. We chose the best model by considering its AIC and RMSE. The best model that is ARIMA (21, 1, 2) with 99% confidence interval. This model is then used to predict the next 15 days (December 09, 2019 to January 02, 2020).
Identifying Fraud in Automobile Insurance Using Naïve Bayes Classifier Dadang Amir Hamzah; Annisa Sentya Hermawan; Shintya Jasmine Pertiwi; Syarifah Intan Nabilah
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.v1i2.3971

Abstract

In this article, the Naïve Bayes Classifier is employed to detect fraud in automobile insurance. The Naïve Bayes classier is a simple probabilistic method based on the Bayes theorem. The data used in this article is determined from databricks.com which consists of 40 attributes and 1000 entries. The target attribute that will be predicted consists of two categories,” yes" or "no", which inform whether there is a fraud or not. The Data is split into training and testing with suitable proportions. Based on training data, the Naïve Bayes Classifier is applied to the testing data and returns the predictions data. Then, the prediction data is compared with the actual data to see the performance of the method. The result shows that the Naïve Bayes Classifier gives a good result to predict the insurance fraud with 78% accuracy, 67% precision, 3% of recall,  and  6% of F1 score  for “Yes”

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