Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score

Abstract

Loan evaluation is an effective method for credit risk assessment in peer-to-peer (P2P) lending and significantly affects lender investment decisions as well as his/her profits. Besides traditional methods of loan evaluation, machine learning has gained increased attention and has achieved better performance for P2P lending, especially regarding the Random Forest approach. However, the loan evaluation model based on Random Forest aims to improve the overall accuracy, which cannot guarantee that the lender profit is maximized when the overall accuracy is maximized because the profits of each loan are different. To further improve the loan evaluation effect and lender profits, Random Forest optimized using a genetic algorithm with profit score (RFoGAPS) is proposed. First, considering the actual and potential returns and losses, a new profit score is proposed and taken as the optimization objective. Second, the genetic algorithm is used to optimize the combination of decision trees in Random Forest. Then, the dataset of Lending Club is used to evaluate the proposed method. Experimental results show that the RFoGAPS can obtain higher profits for lenders compared with actual profit and traditional methods. Some suggestions are proposed based on experimental results to facilitate the healthy development of P2P lending.