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"Asuransi menjadi konsep yang tidak asing lagi dalam memitigasi risiko yang dapat menimbulkan kerugian finansial yang besar bagi pihak tertanggung. Dalam dunia kerja secara khusus, terlihat adanya peningkatan jumlah kecelakaan kerja di Indonesia dari tahun ke tahun. Kecenderungan tersebut memperlihatkan adanya prospek pengembangan asuransi kompensasi pekerja yang menjanjikan. Tentunya, penentuan tarif premi yang cukup sebagai komponen utama dalam kerangka bisnis asuransi memerlukan prediksi severitas klaim yang akurat. Menurut karakteristik data klaim asuransi pekerja, teramati bahwa dataset tersebut berbentuk tabular dan variabel severitas klaim bersifat kontinu. Oleh sebab itu, prediksi severitas klaim dapat dipandang sebagai masalah regresi data tabular. Penelitian ini akan meninjau performa dari TabTransformer, salah satu metode berbasis tranformer dalam melaksanakan regresi yang mengimplementasikan contextual embeddings terhadap fitur-fitur kategorik. Performa sebagai akibat dari penangkapan konteks oleh model TabTransformer akan diukur dan kemudian dibandingkan dengan metode-metode lain yang mendukung penyelesaian permasalahan regresi, seperti Decision Trees Regressor, Random Forest, XGBoost, dan Multi-Layer Perceptron sebagai model dasar TabTransformer.

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It is without the need of doubt to believe upon the integrity within the concepts of insurance as a way of mitigating significant financial risks of its own policyholders. As something which existence is prevalent, risks are also found within the workplace environment as seen in the rising numbers of yearly work-related accidents. This tendency suggests promising prospects upon the development and incorporation of worker’s compensation insurance into the business lines of especially reliable insurance companies. As a core part of insurance policies, determining the sufficient rate of premium would require accurate prediction of claim severity. Upon observing the characteristics of claim severity datasets, witnessed are the following two points: that (1) both datasets take a tabular form, and (2) the number of severities is a continuous target variable. Evidently, it shows that the problem to be solved is regression for tabular data. This particular research will focus upon the performance of TabTransformer as a transformer-based machine learning model that incorporates Transformers in providing a degree of interpretability from its capabilities by performing contextual embeddings of the categorical features of our data. The performance will be measured and will further be compared to other models suitable for regression, such as Decision Trees Regressor, Random Forest, XGBoost, and baseline model Multi-Layer Perceptron"

"Seiring meningkatnya tren kecelakaan kerja selama periode 2007-2017 serta berjalannya kembali kegiatan usaha secara normal pascapandemi COVID-19, lini usaha asuransi kompensasi pekerja menjadi sangat potensial untuk dikembangkan. Sebagai komponen penting dalam model bisnis asuransi, severitas klaim perlu diprediksi seakurat mungkin karena berpengaruh terhadap penetapan tarif premi bagi tertanggung serta bermanfaat dalam mekanisme pengamatan klaim selama proses penyelesaian klaim. Proses prediksi ini dikategorikan sebagai masalah regresi yang biasanya ditangani oleh model-model pembelajaran mesin untuk data tabular. Namun dalam perkembangan studi pembelajaran mesin, terdapat upaya untuk memanfaatkan model Convolutional Neural Network (CNN) untuk melakukan prediksi terhadap data tabular dengan cara mentransformasikan data tersebut ke dalam representasi gambarnya, salah satunya melalui algoritma Image Generator for Tabular Data (IGTD). Penelitian ini bertujuan untuk menguji akurasi model CNN berbasis algoritma IGTD dalam memprediksi klaim asuransi kompensasi pekerja serta membandingkan performa model tersebut dengan model Multi-Layer Perceptron, Random Forest, serta eXtreme Gradient Boosting. Hasil simulasi dengan metode repeated holdout sebanyak lima iterasi menunjukkan bahwa model CNN dapat memprediksi klaim dengan baik meskipun secara umum belum mampu menyaingi model-model non-CNN secara signifikan.

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Along with the increasing trend of work accidents during 2007-2017 period as well as the resumption of business activities normally after the COVID-19 pandemic, the workers’ compensation insurance business line has great potential to be developed. As an important component in the insurance business model, the claim severity needs to be predicted as accurate as possible because it affects the determination of premium rates for the insured and is useful in the claim watching mechanism during the claim settlement process. This prediction process is categorized as a regression problem which is usually handled by machine learning models for tabular data. However, in the development of machine learning studies, there are emerging efforts to utilize the Convolutional Neural Network (CNN) model to predict tabular data by transforming the data into its image representation, one of which is through Image Generator for Tabular Data (IGTD) algorithm. This study aims to test the accuracy of the CNN model based on the IGTD algorithm in predicting workers’ compensation insurance claims and to compare the model performance with the Multi-Layer Perceptron, Random Forest, and eXtreme Gradient Boosting models. The simulation result using the repeated holdout method for five iterations shows that the CNN model can well predict the claims, although in general, it has not been able to significantly compete with non-CNN models."