Credit card fraud forecasting model based on clustering analysis and integrated support vector machine

At present, with the popularization of credit cards, credit card fraud increases gradually. Based on this, this paper designs a credit card fraud prediction model based on cluster analysis and integrated support vector machine using computer technology. First of all, adjust and reduce the Unbalanced...

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Bibliographic Details
Published in:Cluster computing 2019-11, Vol.22 (Suppl 6), p.13861-13866
Main Authors: Wang, Chunhua, Han, Dong
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cluster analysis
Clustering
Commercial banks
Computer Communication Networks
Computer Science
Credit card fraud
Credit card processing
Data mining
Decision trees
Economic impact
Fraud
Injury prevention
Minority & ethnic groups
Operating Systems
Prediction models
Processor Architectures
Support vector machines
Vector quantization
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Summary:At present, with the popularization of credit cards, credit card fraud increases gradually. Based on this, this paper designs a credit card fraud prediction model based on cluster analysis and integrated support vector machine using computer technology. First of all, adjust and reduce the Unbalanced state based on K-means clustering analysis combined with more than half of the random samples. Secondly, the use of the idea of integrated learning to further deal with the Unbalanced state of the data and increase classifier’s awareness of minorities. Finally, we tested the algorithm, and the result showed that the proposed algorithm effectively reduced the cost of accidental injury, which provides a great possibility for the card issuer to effectively reduce the economic losses caused by credit card fraud, which has laid a good theoretical basis and foundation for practical application.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-2118-y