Najdah, Evana Fausta (2025) Analisis Prediksi Financial Distress pada Perusahaan Asuransi Jiwa di Indonesia dengan Metode Generalized Extreme Value (GEV) Regression dan GEV-Stochastic Search Variable Selection (SSVS). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Industri asuransi jiwa di Indonesia kini menghadapi risiko financial distress yang semakin besar akibat berbagai tantangan ekonomi, termasuk ketidakstabilan pasar dan dampak pandemi COVID-19. Penelitian ini berfokus pada upaya memprediksi financial distress di sektor ini dengan menggunakan metode Generalized Extreme Value (GEV) Regression dan GEV-Stochastic Search Variable Selection (SSVS). Kedua pendekatan ini membantu mengidentifikasi variabel-variabel penting dan memberikan prediksi yang lebih akurat. Dengan menggunakan data dari perusahaan asuransi jiwa yang terdaftar di Otoritas Jasa Keuangan (OJK) selama periode 2017–2021, penelitian ini melakukan prediksi financial distress menggunakan metode Generalized Extreme Value Regression (GEV) Regression dan GEV-Stochastic Search Variable Selection (SSVS) dengan melibatkan synthetic features generation secara serentak dan seleksi variabel. Hasil pemodelan GEV-Regression dan GEV-SSVS terbukti mampu memprediksi kemungkinan terjadinya financial distress pada perusahaan asuransi jiwa, dengan keunggulan GEV-SSVS dalam seleksi variabel dan GEV-Regression dalam performa prediksi. Skema Synthetic Feature Generation (SFG) juga menunjukkan kontribusi signifikan dalam meningkatkan kebaikan model. Oleh karena itu, penelitian ini merekomendasikan pengembangan lebih lanjut terhadap cakupan data dan pemanfaatan variabel tambahan untuk meningkatkan kebaikan model. Untuk skema Non-SFG, diperoleh variabel yang signifikan terdapat 3-5 variabel prediktor untuk GEV-Regression, serta 14 variabel prediktor untuk GEV-SSVS. Sedangkan, untuk skema SFG, diperoleh variabel yang signifikan terdapat 8-11 variabel prediktor untuk GEV-Regression, serta 17-24 variabel prediktor untuk GEV-SSVS.
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The life insurance industry in Indonesia is increasingly facing financial distress risks due to various economic challenges, including market instability and the impact of the COVID-19 pandemic. This study focuses on predicting financial distress in this sector using Generalized Extreme Value (GEV) Regression and GEV-Stochastic Search Variable Selection (SSVS) methods. These approaches help identify key variables and provide more accurate predictions. Using data from life insurance companies registered with the Financial Services Authority (OJK) from 2017 to 2021, this study predicts financial distress through GEV Regression and GEV-SSVS, incorporating synthetic feature generation and simultaneous variable selection. GEV-Regression and GEV-SSVS models results have proven effective in predicting the likelihood of financial distress in life insurance companies, with GEV-SSVS offering advantages in variable selection and GEV-Regression showing better predictive performance. The Synthetic Feature Generation (SFG) scheme also significantly contributes to improving model performance. Therefore, this study recommends further development by expanding the data scope and incorporating additional response and predictor variables to enhance model performance. For the Non-SFG scheme, the number of significant predictor variables obtained ranges from 3 to 5 for the GEV-Regression model, and 14 predictor variables for the GEV-SSVS model. Meanwhile, for the SFG scheme, the number of significant predictor variables ranges from 8 to 11 for the GEV-Regression model, and from 17 to 24 predictor variables for the GEV-SSVS model.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Asuransi Jiwa, Financial Distress, Generalized Extreme Value, Financial Distress, Generalized Extreme Value, Life Insurance. |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HC Economic History and Conditions H Social Sciences > HC Economic History and Conditions > HC441 Macroeconomics. H Social Sciences > HG Finance H Social Sciences > HG Finance > HG8051 Insurance H Social Sciences > HG Finance > HG8771 Life insurance |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Evana Fausta Najdah |
| Date Deposited: | 31 Jul 2025 08:49 |
| Last Modified: | 31 Jul 2025 08:49 |
| URI: | http://repository.its.ac.id/id/eprint/125029 |
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