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      Penentuan Premi dan Cadangan Dana untuk Asuransi Decentralized Finance (DeFi) pada Jaringan Ethereum

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      Date
      2026
      Author
      Wijaya, Antonius Aditya Rizky
      Ruhiyat
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      Abstract
      Perkembangan ekosistem Decentralized Finance (DeFi) pada jaringan Ethereum diikuti peningkatan risiko kerugian yang jarang namun berdampak besar. Penelitian ini memodelkan banyaknya dan besarnya kerugian serta membentuk sebaran kerugian agregat melalui collective risk model dan simulasi Monte Carlo menggunakan 174 data insiden kerugian pada periode Februari 2020-Januari 2026. Banyaknya kerugian dimodelkan dengan sebaran binomal negatif dan besarnya kerugian dimodelkan dengan lognormal-generelized Pareto distribution dengan threshold USD 27,000,000, yang terbukti lebih mampu menangkap perilaku ekor dibanding model lognormal tunggal. Simulasi Monte Carlo dengan 100,000 iterasi menghasilkan rata-rata kerugian agregat tahunan USD 646,775,532. Cadangan dana diperoleh sebesar USD 2,347,328,169 (moderat), dan USD 3,831,890,844 (konservatif). Premi diperoleh sebesar 2.52% dan 2.84% dari total nilai aset (berbasis rata-rata), serta meningkat menjadi 6.87% dan 11.21% (berbasis risiko ekor). Temuan ini menunjukkan implikasi signifikan dari ekor berat kerugian DeFi terhadap penentuan premi dan cadangan dana, sekaligus memberikan kerangka kuantitatif yang lebih komprehensif dalam pengelolaan risiko ekosistem DeFi.
       
      The development of the Decentralized Finance (DeFi) ecosystem on the Ethereum network has been accompanied by an increasing risk of rare but high-impact losses. This study models loss frequency and severity and construct the aggregate loss distribution using a collective risk model and Monte Carlo simulation based on 174 loss incidents recorded between February 2020 and January 2026. Loss frequency is modeled using a negative binomial distribution and loss severity is modeled using a lognormal-generalized Pareto distribution with a threshold of USD 27,000,000, which proves more effective in capturing tail behavior than a single lognormal model. Monte Carlo simulation with 100,000 iterations produces an average annual aggregate loss of USD 646,775,532. Reserve funds are estimated at USD 2,347,328,169 (moderate), and USD 3,831,890,844 (conservative). Premium rates are estimated at 2.52% and 2.84% of total asset value (mean-based), and increasing to 6.87% and 11.21% (tail-risk-based). These findings demonstrate the significant implications of heavy-tailed DeFi losses on premium pricing and reserve determination, while providing a more comprehensive quantitative framework for risk management within the DeFi ecosystem.
       
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      http://repository.ipb.ac.id/handle/123456789/173337
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      Indonesia DSpace Group 
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