基于动态网络信息的信用评分

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摘要翻译：
本文通过网络信息的引入来研究信用评分问题，并在两种情况下从理论上探讨了网络信息引入的优势。首先，提出了一个贝叶斯最优滤波器来为贷款人提供风险预测，假设发布的信用评分仅仅是从结构化的金融数据中估计出来的。这样的预测可以作为贷款人未来决策中风险管理的监控指标。其次，进一步提出了一种递推贝叶斯估计方法，通过引入客户间的动态交互拓扑结构来提高信用评分的精度。结果表明，在所提出的演化框架下，所设计的估计器具有比任何有效估计器更高的精度，且在一定的分数范围内，均方误差严格小于客户的Cram\'er-Rao下界。最后，针对一个特殊情况的仿真结果验证了所提算法的可行性和有效性。
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英文标题：
《Credit Scoring by Incorporating Dynamic Networked Information》
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作者：
Yibei Li, Ximei Wang, Boualem Djehiche, Xiaoming Hu
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  In this paper, the credit scoring problem is studied by incorporating networked information, where the advantages of such incorporation are investigated theoretically in two scenarios. Firstly, a Bayesian optimal filter is proposed to provide risk prediction for lenders assuming that published credit scores are estimated merely from structured financial data. Such prediction can then be used as a monitoring indicator for the risk management in lenders' future decisions. Secondly, a recursive Bayes estimator is further proposed to improve the precision of credit scoring by incorporating the dynamic interaction topology of clients. It is shown that under the proposed evolution framework, the designed estimator has a higher precision than any efficient estimator, and the mean square errors are strictly smaller than the Cram\'er-Rao lower bound for clients within a certain range of scores. Finally, simulation results for a special case illustrate the feasibility and effectiveness of the proposed algorithms.
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PDF链接：
https://arxiv.org/pdf/1905.11795

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关键词：信用评分 Contribution Theoretical information interaction 决策 信息 precision 风险管理 评分