A new dynamic modeling framework for credit risk assessment

被引:47
|
作者
Sousa, Maria Rocha [1 ]
Gama, Joao [1 ,2 ]
Brandao, Elisio [1 ]
机构
[1] Univ Porto, Sch Econ & Management, P-4100 Oporto, Portugal
[2] Inst Syst & Comp Engn Technol & Sci, Lab Artificial Intelligence & Decis Support, Oporto, Portugal
关键词
Credit risk modeling; Credit scaling; Dynamic modeling; Temporal degradation; Default concept drift; Memory; SUPPORT VECTOR MACHINE; CLASSIFICATION ALGORITHMS; NEURAL-NETWORKS; SCORING MODELS; CONCEPT DRIFT; PREDICTION; CRITERIA;
D O I
10.1016/j.eswa.2015.09.055
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a new dynamic modeling framework for credit risk assessment that extends the prevailing credit scoring models built upon historical data static settings. The driving idea mimics the principle of films, by composing the model with a sequence of snapshots, rather than a single photograph. In doing so, the dynamic modeling consists of sequential learning from the new incoming data. A key contribution is provided by the insight that different amounts of memory can be explored concurrently. Memory refers to the amount of historic data being used for estimation. This is important in the credit risk area, which often seems to undergo shocks. During a shock, limited memory is important. Other times, a larger memory has merit. An application to a real-world financial dataset of credit cards from a financial institution in Brazil illustrates our methodology, which is able to consistently outperform the static modeling schema. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:341 / 351
页数:11
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