一种基于深度学习的非结构化数据的违约概率预测方法 (2019)
本发明涉及到一种基于深度学习的非结构化数据的违约概率预测方法，该方法包括有：集成和清洗信贷主体人包括文本数据和时序数据在内的非结构化数据；将非结构化数据变换为深度学习模型可识别的数据格式；基于深度学习模型框架，提取数据特征作为样本数据；针对提取出来的样本数据，利用复杂机器学习分类算法-集成树模型构建信用风险模型，输出违约概率预测。本发明的方法通过挖掘文本和时序等非结构化数据，基于深度学习和大数据技术捕捉信贷主体人潜在的风险行为模式，在此之上进行高维数据信用风险建模，实现了对信贷主体人自动、全面、流程化的定量信用风险分析以提升金融风控能力和降低信贷风险。 The invention relates to an unstructured data default probability prediction method based on deep learning. The method comprises the steps as follows: unstructured data, including text data and time series data, of credit subjects are integrated and cleaned; the unstructured data are converted into a data format recognizable by a deep learning model; data features are extracted as sample data on the basis of a deep learning model frame; as for the extracted sample data, a credit risk model is constructed by use of a complex machine learning classification algorithm-integrated tree model, and default probability prediction is output. According to the method, the unstructured data such as text and time sequence data are mined, potential