英文标题：
《Identification of Credit Risk Based on Cluster Analysis of Account
  Behaviours》
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作者：
Maha Bakoben, Tony Bellotti and Niall Adams
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最新提交年份：
2017
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英文摘要：
  Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behavioural cluster analysis using a recently proposed dissimilarity measure of statistical model parameters. The advantage of this new measure is the explicit exploitation of uncertainty associated with parameters estimated from statistical models. Interesting clusters of real credit card behaviours data are obtained, in addition to superior prediction and forecasting of account default based on the clustering outcomes.
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中文摘要：
评估现有信贷账户的风险水平对于实施银行政策和提供金融产品非常重要。本文使用信用卡账户行为的聚类分析来帮助评估信用风险水平。对账户行为进行参数化建模，然后我们使用最近提出的统计模型参数的相异性度量来实施行为聚类分析。这种新方法的优点是明确利用了与统计模型估计的参数相关的不确定性。除了基于聚类结果对账户违约进行卓越的预测和预测外，还获得了真实信用卡行为数据的有趣聚类。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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