Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2016

Abstract

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal credit profiling. LUBD-CM learns users’ hidden behavior habits and topic distributions simultaneously, and represents each user at a much finer granularity. Specifically, we take a real-world Sina Weibo dataset as the testbed for personal credit profiling evaluation. Experiments conducted on the dataset demonstrate the effectiveness of our approach: (1) User credit label can be predicted using LUBD-CM with a considerable performance improvement over state-of-the-art baselines; (2) The latent behavior dimensions have very good interpretability in personal credit profiling.

Keywords

Social networking

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Publication

20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part II

Volume

9652

First Page

130

Last Page

142

ISBN

9783319317496

Identifier

10.1007/978-3-319-31750-2_11

Publisher

Springer International Publishing

City or Country

Auckland, New Zealand

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