International Journal of Quantitative Research and Modeling
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Articles
236 Documents
<![CDATA[Implementation of Ruin Probability Model in Life Insurance Risk Management ]]>
<![CDATA[Lianingsih, Nestia]]>;
<![CDATA[Hidayana, Rizki Apriva]]>;
<![CDATA[Saputra, Moch Panji Agung]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.816
<![CDATA[This study examines the implementation of the ruin probability model in risk management in life insurance companies. The main focus of this study is to evaluate how factors such as initial surplus, premium revenue level, and claim frequency affect the ruin probability of insurance companies. Using the collective risk model approach and relevant claim distribution, this study develops two methods to calculate the ruin probability: an analytical approach and a Monte Carlo simulation. The simulation results show that increasing the initial surplus and premium level significantly reduces the ruin risk, while increasing the claim frequency increases the ruin probability. In addition, the gamma claim distribution is more suitable for modeling claims in life insurance than the exponential distribution. Model validation is carried out by comparing the prediction results with historical data of insurance companies, which shows a high level of accuracy. This study provides important insights for insurance companies in designing more effective and optimal risk management strategies.]]>
<![CDATA[Markov Chain Method for Calculating Insurance Premiums ]]>
<![CDATA[Dihna, Elza Rahma]]>;
<![CDATA[Ismail, Muhammad Iqbal Al-Banna]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.817
<![CDATA[This study applies the Markov Chain method to calculate insurance premiums based on the dynamic health status of policyholders over time. The model considers three health states Healthy, Mild Illness, and Severe Illness each associated with a specific insurance premium. The transition probabilities between these states are represented in a transition matrix, capturing the likelihood of a policyholder remaining in their current health state or transitioning to another state in a given period. Using this approach, the steady-state distribution, which reflects the long-term probabilities of being in each health state, is calculated. This distribution is then used to determine the expected monthly premium by taking a weighted average of the premiums for each state. The methodology incorporates real-world scenarios where a policyholder's health condition may change over time, impacting the premiums they are required to pay. The Markov Chain model provides an effective framework for estimating these premiums by considering the "memoryless" nature of health state transitions, where future states depend only on the current state and not on prior health history. By solving the steady-state equations pi P=pi and ensuring the total probabilities sum to one, the model yields a robust estimation of long-term health state distributions. These distributions, combined with the associated premiums, produce an accurate calculation of expected insurance costs. The results demonstrate the flexibility and accuracy of the Markov Chain method in assessing risks and setting premiums. Insurers benefit from this approach as it enables dynamic pricing strategies tailored to individual risk profiles. For policyholders, the model provides transparency in understanding how health status influences premiums. Overall, this study highlights the practicality of using Markov Chains in health insurance pricing and underscores their importance in creating equitable and sustainable insurance systems.]]>
<![CDATA[Risk Analysis Using Poisson-Pareto Models to Estimate Reserve Funds for Catastrophic Diseases in National Health Insurance ]]>
<![CDATA[Yohandoko, Setyo Luthfi Okta]]>;
<![CDATA[Pangestika, Almira Ajeng]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.818
<![CDATA[Catastrophic diseases such as heart disease, cancer, stroke, and kidney failure pose significant financial burdens on national health insurance systems due to their high treatment costs and frequency. This study utilizes the Poisson-Pareto model to analyze aggregate claims and determine premium loading for these diseases, ensuring the financial sustainability of the National Health Insurance program. Using secondary data from 2018 to 2023, we estimate the parameters for frequency and severity distributions, calculate the expected aggregate claims, and derive the required premium loading at various confidence levels. The results show that heart disease accounts for the highest reserve fund allocation, while kidney failure requires the lowest. These findings emphasize the importance of preparing sufficient reserve funds to manage financial risks associated with catastrophic diseases. The proposed approach provides a robust framework for national health insurance providers to maintain financial stability and optimize resource allocation for high-cost diseases.]]>
<![CDATA[Risk Prediction and Estimation of Corporate Product Claim Reserve Funds in Insurance Companies Using the Extreme Value Theory ]]>
<![CDATA[Maelowati, Indah Dewi]]>;
<![CDATA[Mayaningtyas, Chibi Adinda]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.820
<![CDATA[Every human action involves risk, and in the insurance industry, customer claims are the biggest risk that companies face. This risk must be managed effectively through claim prediction, especially for corporate products. This research analyzes the risk of claims at insurance companies using the Extreme Value Theory (EVT) method, which can estimate extreme risks. Identification of extreme values in claims data is done through the EVT approach, namely Block-Maxima (BM). Generalized Extreme Value (GEV) distribution parameter estimation is performed, followed by prediction of claim risk using Value at Risk (VaR) and estimation of claim reserve funds. The results show that the GEV approach with a 95% confidence level is most suitable for predicting claim risk. Based on these results, the company requires a claim reserve fund of IDR 100,798,248,000 to deal with potential losses due to extreme claims.]]>
<![CDATA[Bankruptcy Risk Analysis in Manufacturing Companies in Indonesia using the Conan & Holder Model, J-UK Model, and Taffler Model ]]>
<![CDATA[Djonaputra, Khalifa Adli]]>;
<![CDATA[Nadira, Rana Syifa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.821
<![CDATA[This study aims to analyze the bankruptcy risk of manufacturing companies in Indonesia using three different bankruptcy prediction models: the Conan & Holder Model, the J-UK Model, and the Taffler Model. To predict the bankruptcy risk with each model, historical financial data from several manufacturing companies listed with the Financial Services Authority (OJK) is used. This research concludes that the combination of these three models provides valuable insights in efforts to enhance the resilience and stability of the manufacturing sector in Indonesia by offering a more comprehensive approach to identifying and managing bankruptcy risks in manufacturing companies. This research is expected to contribute to the development of more effective risk management strategies for the manufacturing industry in Indonesia.]]>
<![CDATA[Comparison of Altman Z-Score Model and Springate Model in Predicting Financial Distress: Case Study of FMCG Companies in Indonesia ]]>
<![CDATA[Dailami, Ahmad]]>;
<![CDATA[Erifianto, Mochamad Kardofa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.822
<![CDATA[Financial distress is a serious threat to the sustainability of a company. This study aims to evaluate the ability of the Altman Z-Score model and the Springate model in predicting the possibility of financial distress in FMCG companies. By comparing the performance of the two methods, this study is expected to provide recommendations for the most appropriate method to use in monitoring the company's financial health. The results of this study have important implications for investors, creditors, and company management in making investment and risk management decisions.]]>
<![CDATA[Comparative Analysis of Altman and Grover's Methods in Predicting Bankruptcy Using the McNemar Test (Case Study: Vehicle Insurance Company in Indonesia) ]]>
<![CDATA[Siahaan, Roy Donald Pangeran]]>;
<![CDATA[Rizqullah, Muhammad Rifan Marsa]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.823
<![CDATA[Vehicle insurance is an important component of automotive financing and consumer protection, which includes various forms of protection that protect the vehicle and its owner. Predicting the bankruptcy of a vehicle insurance company is also very important for vehicle insurance companies to be able to identify potential financial problems as early as possible and take the necessary corrective actions. The Altman and Grover model can be a way to analyze bankruptcy in company. In this study, PT. Asuransi Astra Buana, PT. Allianz Utama Indonesia, PT. Sinar Mas Insurance, and PT. BCA Insurance are used as the analyzed company. The McNemar Test conducted in this study shows that the two methods do not have significant differences in result, so the two methods will relatively have same results.]]>
<![CDATA[The Application of Z-Score and Zavgren Models in Managing Financial Distress at PT Garuda Indonesia (Persero) Tbk ]]>
<![CDATA[Damayanti, Resma]]>;
<![CDATA[Putri, Aulya]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.826
<![CDATA[As an archipelago, the aviation sector in Indonesia plays an important role, but PT Garuda Indonesia (Persero) Tbk. as one of the airline companies has experienced significant financial pressure. In the third quarter of 2023, the company recorded a net loss of US$ 72.07 million. This condition may put the company at risk of financial distress, a situation in which the company experiences financial difficulties before bankruptcy. This study uses the Altman Z-Score Model and the Zavgren Model to predict potential financial distress at PT Garuda Indonesia (Persero) Tbk. The analysis results show that from 2021 to 2023, the Altman Z-Score is consistently in the Bankrupt category, reflecting a high risk of bankruptcy, while the Zavgren model shows vulnerable conditions in 2021 but also indicates bankruptcy in 2022 and 2023. The results of this study are expected to provide early warning and assist management decision-making to reduce the risk of bankruptcy.]]>
<![CDATA[Analysis of the Impact of High Inflation on Present Value Calculation in Investment in Indonesia ]]>
<![CDATA[Azzahra, Syanna Nabila]]>;
<![CDATA[Widiana, Fani Almira]]>;
<![CDATA[Azzahra, Fathimah]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.828
<![CDATA[Inflation is a crucial economic indicator that significantly impacts investment decisions. In Indonesia, inflation has shown substantial fluctuations due to factors such as global commodity prices, monetary policy, and domestic demand. This study aims to analyze the impact of high inflation on present value (PV) calculations in investment contexts, particularly in long-term projects. Present value, a method for assessing future cash flows based on their current value, is influenced by the discount rate, which tends to rise with inflation. Using data from 20152023, this research compares two inflation scenarios (1.56% and 6.38%) and calculates the PV of an investment with a future value of IDR 1,000,000 over 5 years. The results show a significant decrease in PV under high inflation, from IDR 925,497 to IDR 733,999, indicating that inflation erodes the purchasing power of future cash flows. Furthermore, the analysis highlights the more significant impact of inflation on sectors with higher cash flow projections, such as infrastructure. The study underscores the need for investors to consider inflation when making investment decisions to manage risks and maximize returns.]]>
<![CDATA[The Application of Compound Interest in Investment Portfolios ]]>
<![CDATA[Janardana, Komang]]>;
<![CDATA[Wiriandi, Daffa Ibrahim]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v5i4.829
<![CDATA[Effective long-term investment requires a well-structured strategy supported by detailed analysis. The compound interest model serves as a pivotal tool in assessing potential returns on investments by illustrating how interest accumulates on both the initial capital and previously accrued interest. This study delves into the application of compound interest within investment portfolios, aiming to elucidate its impact on long-term growth trajectories. By investigating various factors, such as investment duration and compounding frequency, the research highlights the intricate mechanisms driving investment expansion. A robust understanding of these elements is crucial for making informed financial decisions. The insights gained from this research are intended to equip investors and financial advisors with practical strategies for optimizing portfolio performance and achieving superior investment results, ultimately contributing to the advancement of more sustainable long-term investment practices.]]>
