Abstract
Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.
Highlights
Chinese P2P online lending industry was once without supervision and regulation for more than five years so that most platforms act as credit intermediaries, providing credit enhancement measures such as principal guarantees and third-party guarantees [1, 2]
Accurate evaluation of risk level and operation performances of platforms provides solid basis for practical measures adoption by regulation authorities and acts as an important reference for investors’ decisionmaking. erefore, constructing an advanced evaluation model for P2P online lending platforms is of vital realistic significance [7]
When it evolves to the 26th generation, the ternary classification accuracy reaches 96.7603% and converges to the value. e accuracy is significantly higher than that of single kernel (72.14%–75.59%) and hybrid kernel support vector machines with empirical parameters (76.89%) are presented in Table 4. is shows that the GA optimized hybrid kernel SVM algorithm is effective in accurate classification of risk level and operation quality of Chinese P2P online lending platforms
Summary
Chinese P2P online lending industry was once without supervision and regulation for more than five years so that most platforms act as credit intermediaries, providing credit enhancement measures such as principal guarantees and third-party guarantees [1, 2]. Accurate evaluation of risk level and operation performances of platforms provides solid basis for practical measures adoption by regulation authorities and acts as an important reference for investors’ decisionmaking. With respect to Chinese P2P platforms, existing research studies usually adopt statistical methods such as factor analysis, principal component clustering, and analytic hierarchy process. Yan Xin et al constructed a complex evaluation index system for P2P online loan platforms and utilized the two steps and Kohonen model to cluster 516 platforms for classification and providing references for investors’ decision-making [13]. Applying the GA method and hybrid kernel SVM will reach a higher classification accuracy than statistical and traditional machine learning models and fit for large data volume analysis.
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