Model Analitik Penilaian Risiko Nasabah pada Platform Online Lending Menggunakan Pendekatan Big Data Analytics

Iwan Purwanto, Syandra Sari, Muhammad Umar Arif Paago, Muhammad Tegar Hidayatullah, Muhammad Fikri

Abstract


Perkembangan teknologi finansial (financial technology atau fintech), khususnya platform online lending, telah meningkatkan akses masyarakat terhadap layanan pembiayaan digital. Namun, peningkatan jumlah pengajuan pinjaman juga menimbulkan tantangan dalam proses penilaian risiko kredit, terutama dalam mengidentifikasi calon peminjam yang berpotensi mengalami gagal bayar. Penelitian ini bertujuan untuk mengembangkan model analitik penilaian risiko nasabah pada platform online lending menggunakan pendekatan Big Data Analytics untuk meningkatkan akurasi proses evaluasi kredit. Penelitian ini menggunakan pendekatan kuantitatif berbasis data yang terdiri dari 5 tahapan utama, yaitu (1) pengumpulan data, (2) data preprocessing, (3) integrasi data, (4) pengembangan model analitik, dan (5) evaluasi model. Dataset penelitian mengintegrasikan 3 jenis sumber data utama, yaitu data transaksi peminjam, data profil pengguna, dan data perilaku digital. Penelitian ini membandingkan 4 algoritma machine learning yaitu Logistic Regression, Decision Tree, Random Forest, dan Support Vector Machine (SVM) untuk memprediksi tingkat risiko kredit nasabah. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa terbaik dengan nilai accuracy sebesar 0,91, lebih tinggi dibandingkan Support Vector Machine (0,87), Decision Tree (0,85), dan Logistic Regression (0,82). Analisis feature importance menunjukkan bahwa 3 variabel utama yang paling berpengaruh terhadap prediksi risiko kredit adalah status pembayaran sebelumnya, tingkat pendapatan, dan riwayat pinjaman.

Penelitian ini memberikan kontribusi dengan mengusulkan model analitik berbasis Big Data dan machine learning yang mampu meningkatkan akurasi penilaian risiko kredit pada platform fintech lending. Model yang dihasilkan berpotensi diimplementasikan sebagai sistem pendukung keputusan (decision support system) untuk membantu perusahaan fintech dalam melakukan evaluasi kredit secara lebih cepat, objektif, dan berbasis data.


Keywords


Big Data Analytics, Online Lending, Penilaian Risiko Kredit, Machine Learning, Financial Technology.

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DOI: http://dx.doi.org/10.36448/expert.v15i2.4799

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