Pemanfaatan Data Science Untuk Keputusan Pemberian Kredit Dalam Rangka Minimalisasi Non Performing Loan (Studi Kasus Pt.Bank Central Asia, Tbk Cabang Kisaran)

Abstract

One of the main contributors to a bank’s revenue is credit; therefore, credit risk is one of the key risks that must be appropriately mitigated. The level of credit risk is measured by the Non-Performing Loan ratio, which is a critical indicator of a bank’s financial health. Every credit disbursement must be carried out with prudence. One of the strategic efforts undertaken by BCA Kisaran to support profit growth is to increase its credit portfolio, particularly in the Small and Medium Enterprise Loans and credit decision must be made prudently. Researcher uses Data Science, which focuses on analyzing existing data primarily quantitative data to uncover hidden patterns that can inform strategic decisions. The data analysis in this research will employ Machine Learning Methods with algorithm Random Forest to accelerate the identification of potential customer and the efficiency of the selection process so that each credit decision is made prudently in order to minimize Non-Performing Loan especially Small Medium Enterprise Loans