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This Author published in this journals
All Journal International Journal of Management and Business Economics Journal of Actuarial, Finance, and Risk Managment International Journal of Computing Science and Applied Mathematics-IJCSAM
Maria Yus Trinity Irsan
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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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. 
Claim Reserves Estimation Using Chain Ladder Method in Casualty Insurance for the Period 2010 - 2019 Muara Lysta Sirait; Muhammad Alfarisi; Zievan Ananta Pahlevi; Maria Yus Trinity Irsan
International Journal of Management and Business Economics Vol. 2 No. 3 (2024): June
Publisher : CV Putra Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58540/ijmebe.v2i3.515

Abstract

In a work environment, the presence of risk or unforeseeable events is inevitable, as employees certainly seek to get a sense of security in doing work. Therefore, insurance is responsibility to provide sense of security to employees by providing protection in the form of claim payments to employees who get accidents. To meet the claim payment, insurance companies need to prepare funds. With the chain ladder method, insurance companies can estimate how much funds must be prepared to make claim payments. This study used the secondary data from general insurance companies in the United States published by the National Association of Insurance Commissioners under the title "Statistical Compilation of Annual Statement Information for Property/Casualty Insurance Companies in 2019". Data in the form of cumulative run-off triangle with accident period 2010-2019. Through this calculation, claim reserves that must be prepared by insurance companies for 2020 amounted to USD 1,553,906.
Regime-Switching Value-at-Risk Using Hidden Markov Models: A Cross-Market Analysis of Emerging and Developed Equity Indices Staenly staenly; Maria Yus Trinity Irsan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9691

Abstract

Accurate risk measurement is essential for fi-nancial stability, particularly in markets where returns fre-quently transition between distinct volatility regimes. Thispaper applies a Hidden Markov Model (HMM) to dailylog returns of three equity indices, namely the JakartaComposite Index (JCI), Nikkei 225, and S&P 500, over theperiod January 2008 to December 2024, covering approx-imately 4,100–4,300 observations per index and multiplemajor stress episodes. Gaussian HMMs with two, three,and four hidden states are estimated, with the BayesianInformation Criterion selecting three states for JCI andNikkei 225, and four states for S&P 500, indicating thatoptimal regime granularity is market-specific. A notablestructural finding is that the JCI and S&P 500 transitionmatrices exhibit near-zero probabilities for direct calm-to-stress transitions, implying that crises are preceded byan intermediate regime of elevated uncertainty. Regimeprobabilities are used to construct a regime-conditionalValue-at-Risk (HMM-VaR), benchmarked against Histori-cal Simulation (HS-VaR) and Student-t GARCH (1,1) VaR(t-GARCH-VaR) using the Kupiec unconditional coveragetest and the Christoffersen conditional coverage test. Sub-period analysis at the 95% confidence level reveals thatduring crisis periods, static methods exceed the nominalviolation rate by up to 2.4 times the margin of adaptivemethods, with HS-VaR violation rates reaching 11.88%(JCI) and 13.55% (S&P 500) against a nominal 5%. During normal periods, HMM-VaR is the only methodthat consistently passes both coverage tests across all threemarkets, with violation rates closely approximating thenominal level (4.90%–5.61%). The findings demonstratethat regime-switching VaR provides superior and moreconsistent risk measurement across diverse market struc-tures, while also revealing that aggregate backtesting canbe misleading due to the cancellation of opposing errorsbetween crisis and normal sub-periods.
Co-Authors Elisabeth Gloria Manurung essykapna Randalline Muara Lysta Sirait Muhammad Alfarisi Srava Chrisdes Antoro Staenly staenly Zievan Ananta Pahlevi