Ramalan Skor Kredit Menggunakan Algoritma Pembelajaran Mesin

Khai Wah Khaw

Ramalan Skor Kredit Menggunakan Algoritma Pembelajaran Mesin

Khai Wah Khaw

Abstract

Skor kredit memainkan peranan yang amat penting dalam industri perbankan dan kewangan kerana ia digunakan sebagai alat utama untuk menilai tahap kebolehpercayaan kredit peminjam serta kebarangkalian kegagalan pembayaran balik pinjaman. Penilaian yang tepat membolehkan institusi kewangan mengurangkan risiko kewangan dan pada masa yang sama memaksimumkan pulangan. Seiring dengan pertumbuhan pesat industri kewangan dan peningkatan jumlah data berskala besar, pendekatan tradisional dalam penilaian skor kredit didapati semakin terhad dan kurang berkesan. Kajian ini menggunakan set data sebenar yang diperoleh daripada Kaggle, yang mengandungi 100,000 rekod pelanggan dengan pelbagai ciri demografi dan kewangan. Enam algoritma pembelajaran mesin digunakan, iaitu K-Jiran Terdekat (KNN), Naif Bayes (NB) (NB), Mesin Vektor Sokongan (SVM), Pokok Keputusan (DT), Regresi Logistik (LR), dan Hutan Rawak (RF). Proses kajian melibatkan prapemprosesan data, analisis data penerokaan, pembahagian data, pemodelan serta penilaian prestasi menggunakan metrik ketepatan, ketepatan ramalan, kepekaan, skor F1 dan ROC-AUC. Keputusan menunjukkan bahawa model Hutan Rawak mencapai prestasi terbaik dengan purata skor tertinggi berbanding model lain. Dapatan ini membuktikan potensi pembelajaran mesin sebagai alat sokongan keputusan yang berkesan dalam penilaian risiko kredit bagi industri kewangan.

Keywords

Pembelajaran Mesin; Skor Kredit; Hutan Rawak; Mesin Vektor Sokongan; Pokok Keputusan

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