Bank Account Classification for Gambling Transactions

Catherine,; Denny,; Shihab, Muhammad Rifki

doi:10.1109/eiconcit50028.2021.9431874

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Other2

Book1

Article1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3] proposes a new semi-supervised text mining method, namely the plain collision algorithm, to mine bank risk factors. [4] uses integrated algorithms such as LightGBM for classification to identify gambling accounts. [5] utilizes logistic regression algorithms to detect suspicious transactions in bank accounts.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Ke²,

Yu³

et al. 2022

Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%