Research on Building Bank Anti-fraud Model Based on Tri-training Semi-Supervised Learning and Fuzzy SVM Active Learning

DOI

10.2991/wartia-17.2017.71How to use a DOI?

Keywords

semi-supervised learning, active learning, tri-training, fuzzy SVM, anti-fraud.

Abstract

With the rapid development of Internet finance and its applications, online banking fraud is becoming increasingly frequent. How to accurately identify the fraud data among the huge amount of transactions, is the urgent needs of Third Party Payment Center, Channel Department and other departments of banks. As to this problem, this paper proposes a recognition method based on tri-training semi-supervised learning and fuzzy SVM active learningaiming at researching thathow to build aneffectiveanti-fraud model for banks. Experimental results show that the method has encouraging recognition accuracy, which provides an effective scheme for banks’ anti-fraud model training, building and fraud recognition.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoguo Wang
AU  - Luxi Liu
PY  - 2017/11
DA  - 2017/11
TI  - Research on Building Bank Anti-fraud Model Based on Tri-training Semi-Supervised Learning and Fuzzy SVM Active Learning
BT  - Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
PB  - Atlantis Press
SP  - 369
EP  - 373
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-17.2017.71
DO  - 10.2991/wartia-17.2017.71
ID  - Wang2017/11
ER  -