Penerapan Metode Random forest untuk Analisis Risiko pada dataset Peer to peer lending

Abstract

Abstract — Peer to peer lending (P2PL) is one of financial technology (fintech) that develops very fast in society. On the other side, P2PL project has many risks. The risk of  P2PL project can be analyzed using classification. There are two conditions of a loan, namely a good loan and a bad loan. This study uses two methods to analyze a P2PL dataset, that are Random Forest method and Logistic Regression method. Data is taken from P2PL loan dataset provided by Data World, which contains  887.379 entries with 74 features. The result of experiments is a model that can be used to predict and classify a P2PL loan as a good or bad one.
 
Keywords— Fintech; Logistic Regression; Peer to peer lending; Random forest