Identification Model for Gambling and Fraud in BankPersonal Settlement Accounts Based on XGBoost Algorithm

DOI

10.2991/978-94-6463-108-1_63How to use a DOI?

Keywords

data mining; anti-gambling and anti-fraud; XGBoost

Abstract

Based on the basic characteristics of sample accounts involved in gamblingand fraud, this paper constructs a risk identification model for gamblingand fraud in bank personal settlement accounts through machine learning algorithms,uses the model to accurately identify suspected fraudulent accounts, andanalyzes risks through key features of gambling and fraud. The rules of accountbehavior provide decision support for the establishment of anti-fraud models. Themodel proposed in this paper can enhance the recognition accuracy and predictabilitybased on the collision rules, and effectively avoid the problem of blockingnormal accounts by mistake.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xin Li
AU  - Yan Ke
AU  - Jianbin Yu
AU  - Yao Zhang
PY  - 2022
DA  - 2022/12/30
TI  - Identification Model for Gambling and Fraud in BankPersonal Settlement Accounts Based on XGBoost Algorithm
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 556
EP  - 563
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_63
DO  - 10.2991/978-94-6463-108-1_63
ID  - Li2022
ER  -