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

• 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
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-018-2118-y

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.