论文标题
PSD2可解释的信用评分AI模型
PSD2 Explainable AI Model for Credit Scoring
论文作者
论文摘要
该项目的目的是开发和测试先进的分析方法，以提高信用风险模型的预测准确性，同时保留模型的解释性。特别是，该项目着重于将可解释的机器学习模型应用于与银行相关的数据库。输入数据是从开放数据中获得的。在整个验证的模型中，Catboost表现出最高的性能。调整超参数后，该算法实现产生的GINI为0.68。 Shap软件包用于提供对模型预测的全球和局部解释，以制定一种理解决策者算法的人类全面方法。使用Shapley值选择了20个最重要的特征，以呈现一个完整的人类理解模型，该模型揭示了个人的属性如何与其模型预测相关。
The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to bank-related databases. The input data were obtained from open data. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.68 after tuning the hyper-parameters. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.
