PFI:BIC Fraud Detection via Visual Analytics: An Infrastructure to Support Complex Financial Patterns (CFP)-based Real-Time Services Delivery

PFI：通过视觉分析进行 BIC 欺诈检测：支持基于复杂金融模式 (CFP) 的实时服务交付的基础设施

• 批准号: 1430144
• 负责人: Hasan Davulcu
• 金额: $ 80万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430144&HistoricalAwards=false
• 关键词: PFI; BIC; Fraud; Detection; via

This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project from Arizona State University focuses on building a platform that will integrate data from multiple sources and explore data analysis techniques that can more accurately detect indications of financial fraud. According to Federal Trade Commission's (FTC) annual "Consumer Sentinel Network Data Book", the most comprehensive database of U.S. fraud trends, American consumers submitted more than 1.5 million complaints - a 62 percent increase in just three years, and they reported losing over $1.6 billion to fraud in 2013. Detecting increasingly complex fraud schemes requires services that are able to integrate and enrich data from disparate financial and other data sources and hunt for recurring and often interconnected anomalous patterns in large networks. The proposed platform will enable integration and enrichment of limited private financial data with larger publicly available data sets to detect fraud and reduce losses due to fraudulent transactions. The project will also include training and research experience for undergraduate and graduate students.The data linkage and financial pattern discovery platform which is to be developed via visual analytics will enable "smart" fraud detection and prevention services. Today, in order to obtain a single unified view of fraud activity across the enterprise and manage fraud on a cross-institution basis, fraud detection companies collect, verify, and analyze consumer data and financial information. Researchers recognize, however, that new insights into fraud and risk patterns require the ability to integrate financial data with domain independent data through real-time entity/identity discovery, resolution, cross-linking and schema mapping techniques. Therefore, the importance of the research discovery underpinning this project includes solving platform and processing challenges that arise from the need to integrate, filter, analyze, and visualize, in a secure and scalable manner, large private knowledge networks, also incorporating uncontrolled, unrestricted, untrusted, unstructured and unpredictable data from external domains. The ability to treat together financial and domain independent data will lead to enriched unified data, unprecedented predictive accuracy in fraud prevention and detection, and an entirely new suite of risk management services and products.The partners at the inception of the project are Arizona State University (ASU) (School of Computing, Informatics, and Decision Systems Engineering and, notably, the investigators also are members of ASU's Information Assurance Center, certified by NSA and DHS); and a small business, Early Warning Services, LLC (Scottsdale, AZ).

这种创新伙伴关系：亚利桑那州立大学的建设创新能力（PFI：BIC）项目专注于建立一个平台，该平台将整合来自多个来源的数据，并探索数据分析技术，可以更准确地发现金融欺诈的迹象。 根据联邦贸易委员会（FTC）年度《消费者哨兵网络数据手册》（美国欺诈趋势最全面的数据库），美国消费者提交了150多万起投诉--短短三年内增长了62%，他们报告说，2013年因欺诈损失超过16亿美元。检测日益复杂的欺诈计划需要能够整合和丰富来自不同金融和其他数据源的数据的服务，并在大型网络中寻找重复出现且通常相互关联的异常模式。拟议的平台将能够将有限的私人金融数据与更大的公开数据集整合和丰富，以检测欺诈并减少欺诈交易造成的损失。该项目还将为本科生和研究生提供培训和研究经验。通过可视化分析开发的数据链接和金融模式发现平台将实现“智能”欺诈检测和预防服务。如今，为了获得整个企业欺诈活动的统一视图并跨机构管理欺诈，欺诈检测公司收集、验证和分析消费者数据和财务信息。然而，研究人员认识到，对欺诈和风险模式的新见解需要能够通过实时实体/身份发现、解析、交叉链接和模式映射技术将金融数据与独立于领域的数据相集成。因此，支持该项目的研究发现的重要性包括解决平台和处理挑战，这些挑战来自于以安全和可扩展的方式集成，过滤，分析和可视化大型私有知识网络的需求，还包括来自外部域的不受控制，不受限制，不可信，非结构化和不可预测的数据。将财务数据和独立于领域的数据一起处理的能力将带来丰富的统一数据，在欺诈预防和检测方面前所未有的预测准确性，以及一套全新的风险管理服务和产品。该项目的合作伙伴是亚利桑那州州立大学（ASU）（计算，信息学和决策系统工程学院，特别是调查人员也是ASU信息保障中心的成员，由NSA和DHS认证）;和一个小企业，预警服务有限责任公司（斯科茨代尔，亚利桑那州）。