摘要
逾期风险控制是信用贷款服务的关键业务环节,直接影响放贷企业的收益率和坏账率。随着移动互联网的发展,信贷类金融服务已经惠及普罗大众,逾期风控也从以往依赖规则的人工判断,转为利用大量客户数据构建的信贷模型,以预测客户的逾期概率。相关模型包括传统的机器学习模型和深度学习模型,前者可解释性强、预测能力较弱;后者预测能力强、可解释性较差,且容易发生过拟合。因此,如何融合传统机器学习模型和深度学习模型,一直是信贷数据建模的研究热点。受到推荐系统中宽度和深度学习模型的启发,信贷模型首先可以使用传统机器学习来捕捉结构化数据的特征,同时使用深度学习来捕捉非结构化数据的特征,然后合并两部分学习得到的特征,将其经过线性变换后,最后得到预测的客户的逾期概率。所提模型中和了传统机器学习模型和深度学习模型的优点。实验结果表明,其具有更强的预测客户逾期概率的能力。
Default risk control is a key business component of credit loan services,which directly affects the profitability and bad-debt rate of lenders.With the development of the mobile Internet,credit-based financial services have benefited the general public.Default risk control has changed from manual judgment based on rules to credit models built by using large amounts of customer data to predict the default rate of customers.Relevant models include traditional machine learning models and deep learning mo-dels.The former has a strong interpretability but a weak predictability;the latter has a strong predictability but a poor interpre-tability,which is prone to overfitting the training data.Therefore,the integration of traditional machine learning models and deep learning models has always been an active research area in credit modeling.Inspired by the wide&deep learning models in re-commendation systems,a credit model first can utilize traditional machine learning to capture features of the structured data,while a deep learning can capture features of the unstructured data.Then,the model combines two parts of the learned features and uses an additional linear layer to transform the hidden features.Finally,the model outputs the predicted default rate.This model neutralizes the advantages of traditional machine learning models and deep learning models.Experimental results show that the proposed model has a stronger capability to predict the default probability of customers.
作者
宁婷
苗德壮
董启文
陆雪松
NING Ting;MIAO De-zhuang;DONG Qi-wen;LU Xue-song(School of Data Science and Engineering,East China Normal University,Shanghai 200062,China)
出处
《计算机科学》
CSCD
北大核心
2021年第5期197-201,共5页
Computer Science
基金
国家自然科学基金(U1711262,61672234)。
关键词
逾期风险预测
机器学习
深度学习
宽度和深度模型
Default risk control
Machine learning
Deep learning
Wide&deep learning models
