The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept...The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.展开更多
The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credi...The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.展开更多
文摘The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.
基金supported by grants from Major Program of National Social Science Foundation(No.22&ZDo73)the key program of the National Natural Science Foundation of China(NSFC No.71631005).
文摘The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.
