Predicting mortgage default using convolutional neural networks

被引：78

作者：

Kvamme, Havard ^{[1
]}

Sellereite, Nikolai ^{[2
]}

Aas, Kjersti ^{[2
]}

Sjursen, Steffen ^{[3
]}

机构：

[1] Univ Oslo, Dept Math, Niels Henrik Abels Hus Moltke Moes Vei 35, N-0851 Oslo, Norway

[2] Norwegian Comp Ctr, Stat Anal Machine Learning & Image Anal, Gaustadalleen 23a, N-0373 Oslo, Norway

[3] DNB ASA, Grp Risk Modelling, Dronning Eufemias Gate 30, N-0191 Oslo, Norway

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2018年 / 102卷

关键词：

Consumer credit risk; Machine learning; Deep learning; Mortgage default model; Time series; CLASSIFICATION ALGORITHMS; CREDIT; RISK; PERFORMANCE; MODELS; AREA;

D O I：

10.1016/j.eswa.2018.02.029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：207 / 217

页数：11

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