论文标题
通过可解释的AI分析信用评分的机器学习模型并优化投资决策
Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions
论文作者
论文摘要
本文研究了与可解释的AI（XAI）实践有关的两个不同但相关的问题。机器学习（ML）在金融服务中越来越重要，例如预批准，信用承销，投资以及各种前端和后端活动。机器学习可以自动检测培训数据中的非线性和相互作用，从而促进更快，更准确的信用决策。但是，机器学习模型是不透明的，难以解释，这是建立可靠技术所需的关键要素。该研究比较了各种机器学习模型，包括单个分类器（逻辑回归，决策树，LDA，QDA），异质集合（Adaboost，随机森林）和顺序神经网络。结果表明，合奏分类器和神经网络的表现要优于表现。此外，使用总部位于美国的P2P贷款平台Lending Club提供的开放式访问数据集，利用两种先进的事后不可解释性解释性技术 - 石灰和外形来评估基于ML的信用评分模型。在这项研究中，我们还使用机器学习算法来开发新的投资模型，并探索可以最大程度地提高盈利能力同时最大程度地降低风险的投资组合策略。
This paper examines two different yet related questions related to explainable AI (XAI) practices. Machine learning (ML) is increasingly important in financial services, such as pre-approval, credit underwriting, investments, and various front-end and back-end activities. Machine Learning can automatically detect non-linearities and interactions in training data, facilitating faster and more accurate credit decisions. However, machine learning models are opaque and hard to explain, which are critical elements needed for establishing a reliable technology. The study compares various machine learning models, including single classifiers (logistic regression, decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest), and sequential neural networks. The results indicate that ensemble classifiers and neural networks outperform. In addition, two advanced post-hoc model agnostic explainability techniques - LIME and SHAP are utilized to assess ML-based credit scoring models using the open-access datasets offered by US-based P2P Lending Platform, Lending Club. For this study, we are also using machine learning algorithms to develop new investment models and explore portfolio strategies that can maximize profitability while minimizing risk.
