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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics Premium: Insurance Business Journal Jurnal Pengabdian Masyarakat Bangsa RENATA Jurnal Pengabdian Masyarakat Kita Semua Indonesian Journal of Applied Mathematics Jurnal KALANDRA TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Dwi Mahrani
Actuarial Science Study Program, Faculty Of Science, Institut Teknologi Sumatera, Indonesia
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A COMPARATIVE STUDY OF IBNR CLAIM RESERVE ESTIMATION USING BENKTANDER, WALTER NEUHAUS, AND OPTIMAL CREDIBILITY LOSS RATIO APPROACHES Dwi Mahrani; Edward Al Faruq Purba
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp1893-1910

Abstract

Reinsurance plays a crucial role in risk transfer for insurance companies, particularly in managing large and volatile losses. One of the key challenges in reinsurance is the accurate estimation of Incurred But Not Reported (IBNR) claim reserves, especially for nonproportional assumed property business, which is characterized by high claim volatility and delayed reporting patterns. This study provides an empirical comparison of credibility-based reserving methods—namely the Benktander and Walter Neuhaus approaches—using reported claims and earned premium data from United States reinsurance companies for the period 2010–2019. Unlike most existing studies that focus on proportional or direct insurance portfolios, this research evaluates the performance of these methods in a nonproportional reinsurance context and benchmarks them against the Optimal Credibility Loss Ratio method, which minimizes Mean Squared Error (MSE). Claim reserves are estimated using run-off triangle techniques, loss development factors, and credibility weighting schemes, and the accuracy of each method is assessed through MSE ratios. The results show that the Benktander method produces reserve estimates that are consistently closer to the optimal benchmark, with an average MSE ratio of 1.0265, compared to 1.4184 for the Walter Neuhaus method. These findings indicate that the Benktander approach offers a more stable and statistically efficient reserve estimation for immature and volatile nonproportional reinsurance data. The study contributes to actuarial reserving literature by providing empirical evidence on the relative effectiveness of credibility-based methods and offering practical insights for actuaries in selecting appropriate IBNR reserving techniques under high uncertainty.
Comparative Modeling of Pineapple Production Using Gaussian GLM and Random Forest Regression Radot MH Siahaan; Indah Gumala Andirasdini; Fuji Lestari; Dwi Mahrani; Amalia Listiani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28721

Abstract

This study aims to conduct a comparative modelling of pineapple production at PT Great Giant Pineapple (GGP) using Gaussian GLM as parametric statistical approach and Random Forest Regression method as machine learning based on monthly data from 2014 to 2022. Multicollinearity testing and distribution fitting were conducted to validate the Gaussian assumption. For the Random Forest Regression, hyperparameters were optimized by tuning the number of trees (ntree) and the number of predictors at each split (mtry) with model stability evaluated using Out-of-Bag (OOB) error. The Gaussian GLM achieved a MAPE of 8.41% (R² = 0.106) for the GP3 clone and 11.27% (R² = 0.149) for the F180 clone. Random Forest Regression produced a testing MAPE of 9.28% (R² = 0.144) for GP3 and 12.11% (R² = 0.105) for F180. While both models achieved low prediction error based on MAPE, they differed in identifying influential variables and showed limited explanatory power as indicated by low R² values. The Gaussian GLM identifies air pressure as significant for both clones and rainfall for F180 clone, while Random Forest consistently identifies rainfall as the most influential predictor. These findings confirm the complementary strengths of parametric and machine learning approaches in supporting climate-based production planning and risk mitigation.
Actuarial Evaluation of Additional Contributions in Early Retirement Programs Using the Spreading Gains and Losses Method Dwi Mahrani; Miftha Ulya Nazima; Ayu Sofia; Tiara Yulita
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28726

Abstract

This study examines the actuarial and funding implications of accelerated retirement in a defined benefit pension scheme by integrating the Projected Unit Credit (PUC) method with the Spreading Gains and Losses approach. While both methods are widely applied in pension valuation, limited empirical evidence evaluates their combined implementation under retirement age acceleration scenarios, particularly in Indonesian public sector schemes. This study addresses that gap using secondary administrative employment data of 87 female civil servants obtained from the Investment and One-Stop Integrated Services Office of Lampung Province (Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Provinsi Lampung), grouped into four entry-age cohorts (22–25 years). The analysis compares normal retirement at age 58 with accelerated retirement at age 50, assuming a 5% annual effective interest rate and 8% biennial salary growth. The results indicate that, at valuation age 45, actuarial liabilities increase by approximately 49.8% under retirement at age 50 relative to age 58. The shorter discounting period and earlier benefit payments outweigh the reduced contribution period, resulting in the emergence of Unfunded Actuarial Liability (UAL). The resulting Past Service Liability (PSL) is amortized over five years, requiring additional contributions ranging from IDR 27.06 million to IDR 82.05 million across entry-age groups. These findings highlight the high sensitivity of pension funding to retirement age assumptions and emphasize the importance of actuarial impact assessments prior to policy implementation. However, the deterministic framework and relatively small sample size limit broader generalization of the results.
Co-Authors Agus Irawan Alfarizi , Mhd Rayhan Jovely Alfian, Muhammad Nuril Andi Fitriawati Ayu Sofia Ayu Sofia Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dani Al Mahkya Dewi, Karina Sylfia Dila Tirta Julianty Edward Al Faruq Purba Fitriawati , Andi Fuji Lestari Fuji Lestari Gustina K. S, Annisa Hevita Hayati MT, Ma’rufah Indah Gumala Andirasdini Irfan, Miftahul Irwan, Sri Efrinita Lestari, Yushinta Cahya Listiani, Amalia Listriani, Amalia Madonna, Nora Marisa Marisa Miftha Ulya Nazima Mt, Ma’rufah Hayati Nasrullah Nasrullah Nasrullah Pribadi, Aswan Anggun Radot MH Siahaan Rifky Fauzi Rivai, Muklas Rosni, Rosni Siahaan, Marco Marolop Simanjuntak, Erica Grace Siregar, Abian Avisena Syafitri, Nabilah Theo Markus Tiara Shofi Edriani Tiara Yulita Wulandari , Winda Yulita, Tiara