CNN-based feature cross and classifier for loan default prediction

Shizhe Deng; Rui Li; Yaohui Jin; Hao He

doi:10.1117/12.2579457

10 November 2020 CNN-based feature cross and classifier for loan default prediction

Shizhe Deng, Rui Li, Yaohui Jin, Hao He

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841K (2020) https://doi.org/10.1117/12.2579457
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Loan default prediction has been playing a key role in credit risk management throughout the years. Existing solutions usually involve classical machine learning classifiers, e.g. logistics and SVM, but most of them need extensive feature engineering such as feature cross which requires plenty of hand-crafted feature design. In this paper, we propose a novel method to implement feature cross based on the convolutional neural network. This method is designed to extract automatically important cross features and generate cross-feature embedding from structured data which reduces the need to generate hand-crafted cross features. The experimental results show that our method can improve the performance of predicting loan default probability compared with the methods based only on classical machine learning algorithms that are widely used in loan default prediction.

Citation Download Citation

Shizhe Deng, Rui Li, Yaohui Jin, and Hao He "CNN-based feature cross and classifier for loan default prediction", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841K (10 November 2020); https://doi.org/10.1117/12.2579457

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available