机器学习在重点领域信用风险分类应用研究——以电梯维保企业为例

Research on the Application of Machine Learning in the Supervision of Credit Risk Classifi cation in Key Fields-Taking Elevator Maintenance Enterprises as An Example

国家高度重视涉及人民群众生命财产安全的电梯等特种设备重点领域安全监管。面对目前电梯保有量持续增长、电梯安全监管形势复杂的情况,市场监管部门提出了对电梯维保企业实施信用风险分类监管的需求。本文在研究通用型企业信用风险分类模型的基础上,考虑电梯专业领域风险因素,并引入机器学习算法构建了电梯维保企业信用风险分类指标体系和模型。通过H省电梯维保企业的验证结果发现,该模型结果能够较好地反映电梯维保企业存在的问题,能够有效提高监管及时性、精准性、有效性,有助于合理配置监管资源并提升监管效能,推动监管更加公平有效。

China has attached great importance to safety supervision in key areas of special equipment such as elevators involving the safety of people’s lives and property.Faced with the continuous growth of elevators and the complex situation of elevator safety supervision,market regulation departments have proposed the need to implement credit risk classifi cation supervision of elevator maintenance enterprises.On the basis of research on general enterprise credit risk classification model,this paper considers the risk factors in the fi eld of elevator,and establishes a credit risk classifi cation index system and model for elevator maintenance enterprises by introducing machine learning algorithms.The verifi cation results of elevator maintenance enterprises in H province indicate that the model can well refl ect the problems existing in elevator maintenance enterprises,effectively improve the timeliness,accuracy,and effectiveness of supervision,which helps to allocate regulatory resources reasonably,improves regulatory effi ciency,and promotes fairer and more effective supervision.