Credit scoring analysis using pseudo nearest neighbor. (May 2019)

• Record Type: Journal Article
• Authors: Pratiwi, H; Mukid, M A; Hoyyi, A; Widiharih, T
• Is Part Of: Journal of physics. Volume 1217(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1217/1/012100
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Credit scoring is one of the crucial task and a core responsibility for financial institutions in their risk management. This study aims to apply the pseudo nearest neighbour (PNN) method as a tool to identify which prospective borrowers are eligible for their loan proposals. If a new borrower has characteristics closer to a good historical borrower then the loan proposal is worthy to approval. But if not, the proposed loan will be refused. The historical data in this paper are credit data from a national bank in Indonesia. The characteristics of historical debtors consist of age, amount of a child, length time of business, income, loans amount, and the period of credit. The best classification of k-NN is using k = 1, because it makes the smallest error 1, 89%. While the best classification of PNN is using k = 13 with the smallest error 20, 75%. Based on total accuracy of classification shows that the credit classification of debtors using k-NN is more appropriate than PNN.