通过将图形数据库与机器学习相结合,加强银行业欺诈检测
Ayushi Patil1, Shreya Mahajan1, Jinal Menpara1
1Artificial Intelligence & Machine Learning Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra 412115, India.
MethodsX
|April 16, 2024
概括
本研究介绍了一种基于图形的机器学习模型,用于检测银行欺诈. 该模型有效地识别了各种银行业务中的欺诈性交易,提高了数字时代的财务安全.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 金融技术 金融技术
背景情况:
- 银行业的数字化转型增加了欺诈风险.
- 传统的欺诈检测方法与不断发展的网络威胁作斗争.
- 需要先进的欺诈检测和预防系统.
研究的目的:
- 实施最先进的图形数据库方法来检测欺诈性交易.
- 开发和评估基于图形的机器学习模型,用于实时欺诈检测.
- 提高银行业务的安全性,防止复杂的欺诈策略.
主要方法:
- 使用Neo4j图形数据库建模关系特征.
- 应用基于图形的机器学习来检测异常和模式.
- 使用准确度,回忆,假阳性率和ROC曲线评估系统性能.
主要成果:
- 拟议的基于图形的模型在检测欺诈活动方面表现出有效性.
- 该方法提供交易和用户行为的立即分析.
- 该方法为评估欺诈检测系统提供了可靠的框架.
结论:
- 像图形数据库这样的创新技术对于现代欺诈检测至关重要.
- 该研究验证了拟议的基于图形的机器学习模型的有效性和可靠性.
- 金融机构可以利用这种方法来加强其欺诈预防策略.
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