基于Logistic回归的中小企业信贷风险评估与信贷策略优化建模

Creditrisk assessment based on Logistic regression and credit strategy optimization modeling of small and medium-sized enterprises

为了便于银行对中小企业进行信贷风险评估,同时制定最优信贷策略,利用企业与上下游合作伙伴的银行流水信息,构建企业营业收入能力、盈利能力、客户稳定性、交易活力4个一级指标组成的风险评估指标体系;基于Logistic回归对企业信贷风险进行预测,并与误差反向传播神经网络进行了对比分析;结合违约概率与不同利率下的留存率,以银行对中小企业的最大化期望收益为目标函数,建立信贷策略优化模型;对信贷风险评估和信贷策略优化模型分别进行实证分析,验证模型的有效性。结果表明:Logistic回归具有较高的准确率和查全率,评估指标受试者工作特征曲线下面积达到0.964,适合中小企业的信贷风险预测和评估;所建立的信贷策略优化模型能确定每个贷款企业的贷款额度和贷款利率,并使银行期望收益达到最大。

In order to facilitate banks to assess the credit risk of small and medium-sized enterprises,and formulate the optimal credit strategy,an indicator system composed of four primary risk assessment indicators,namely,business income ability,profitability,customer stability,and transaction vitality,is constructed by using the bank flow information of enterprises with upstream and downstream partners.The enterprise credit risk is predicted based on Logistic regression,and compares it with error back-propagation neural network.Combining the probability of default and the retention rate under different interest rates,taking the maximum expected return of banks on small and medium-sized enterprises as the objective function,the credit strategy optimization model is established.In order to verify the effectiveness of the model,the credit risk assessment and credit strategy optimization models are empirically analyzed.The results indicate that Logistic regression has high accuracy and recall,and the area under the curve of receiver operating characteristic reaches 0.964,which is suitable for credit risk prediction and assessment of small and medium-sized enterprises.The credit strategy optimization model can determine the loan amount and loan interest rate of each lending enterprise,and maximize the expected return of the bank.