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Explainable Machine Learning Models of Consumer Credit Risk 消费者信贷风险的可解释机器学习模型
Pub Date : 2023-10-04 DOI: 10.2139/ssrn.4006840
Randall Davis, Andrew W. Lo, Sudhanshu Mishra, Arash Nourian, Manish Singh, Nicholas Wu, Ruixun Zhang
In this work, the authors create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end user. They analyze the explainability for various stakeholders: loan companies, regulators, loan applicants, and data scientists, incorporating their different requirements with respect to explanations. For loan companies, they generate explanations for every model prediction of creditworthiness. For regulators, they perform a stress test for extreme scenarios. For loan applicants, they generate diverse counterfactuals to guide them with steps toward a favorable classification from the model. Finally, for data scientists, they generate simple rules that accurately explain 70%–72% of the dataset. Their study provides a synthesized ML explanation framework for all stakeholders and is intended to accelerate the adoption of ML techniques in domains that would benefit from explanations of their predictions.
在这项工作中,作者创建了机器学习(ML)模型,利用现实世界的数据集预测个人房屋净值信贷风险,并演示了解释这些 ML 模型输出的方法,使最终用户更容易理解这些模型。他们分析了不同利益相关者(贷款公司、监管机构、贷款申请人和数据科学家)的可解释性,并结合了他们对解释的不同要求。对于贷款公司,他们为每个信用度模型预测生成解释。对于监管机构,他们要对极端情况进行压力测试。对于贷款申请人,他们会生成不同的反事实,以指导他们从模型中获得有利的分类。最后,对于数据科学家,他们生成的简单规则可以准确解释 70%-72% 的数据集。他们的研究为所有利益相关者提供了一个综合的 ML 解释框架,旨在加快 ML 技术在各领域的应用,这些领域将受益于对其预测的解释。
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引用次数: 0
Waarom artikel 12 IORP II in strijd is met het EU Verdragsrecht (Why Article 12 IORP II Conflicts with EU Treaty Law) 为什么第12条IORP II违反欧盟条约法(为什么第12条IORP II违反欧盟条约法)
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4589452
Hans van Meerten
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引用次数: 0
Green Bond Issuance and Air Quality Improvement: Chinese and International Evidence 绿色债券发行与空气质量改善:中国与国际证据
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4601294
Tse-Chun Lin, Yan Luo, Grace Tian, Hao Yang
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引用次数: 0
The Topography of Nations 国家地形
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4616873
Treb Allen
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引用次数: 0
A Practical Mapping of Australian Income Tax Implications for DeFi 澳大利亚所得税对DeFi影响的实际映射
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4588323
Elizabeth Morton
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引用次数: 0
Hunting for Employees, Employers, Independent Contractors, Dependent Contractors, Independent Employees, Workers, and Other Figments of the Legal Imagination 寻找雇员、雇主、独立承包商、独立承包商、独立雇员、工人和其他法律想象的虚构人物
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4587945
Brian A. Langille, Ben Mayer-Goodman
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引用次数: 0
Demolishing Homes for Air Cargo: San Bernardino International Airport Gateway Plan 拆除房屋空运货物:圣贝纳迪诺国际机场门户计划
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4587209
Daniel Flaming, Patrick Burns
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引用次数: 0
Binary Fallacy to Geopolitics: What about African perspectives? 地缘政治的二元谬误:非洲的前景如何?
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4609235
Siyabonga Hadebe
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引用次数: 0
AI Mapping for Hydrones and Drones 人工智能地图的hydrone和无人机
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4605916
Mario Arturo Ruiz Estrada
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引用次数: 0
Global Value Chains and International Risk Sharing 全球价值链与国际风险分担
Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4617306
Giancarlo Corsetti, Lucio D’Aguanno, Aydan Dogan, Simon Lloyd, Rana Sajedi
Unlike final-goods trade, intermediate-input trade through Global Value Chains (GVCs) creates supply-side linkages across borders. We show that, even when GVCs themselves are efficient, they have welfare implications because these linkages affect countries’ ability to share risks in incomplete financial markets. This novel interaction between trade and finance arises from a distinct channel of cross-border transmission with GVCs, the marginalcost effect. Productivity shocks, by moving relative prices, impact the marginal cost of production both at home and abroad, and therefore, in equilibrium, their relative supply. When international financial markets are incomplete, these supply-side linkages will affect household wealth in both countries, and, in turn, the degree of international risk sharing. The direction and strength of this effect varies with the trade elasticity and the degree of GVC integration, with non-monotonic effects leading to ‘fragmentation traps’ in which small increases in GVC integration reduce risk sharing, while large increases would improve it. We show that in the quantitatively relevant case, GVCs reduce cross-border misalignments, and so endogenously support international risk sharing.
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引用次数: 0
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