Computational efficiencies in insurance fraud data analysis

保险欺诈数据分析的计算效率

• 批准号: 460882-2013
• 负责人: Lu, Fletcher
• 金额: $ 4.37万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=597834
• 关键词: Computational; efficiencies; insurance; fraud; data

As with many large medical and life insurance business, Medavie Blue Cross has developed a large repository of information from multiple database sources that come from their diversity of clients such as pharmacies, physicians' offices, and clinics, which is continuously being updated with new monthly information for its billing and claims process. Such a wealth of information may be explored to find new patterns of fraud. However this wealth and diversity of data also presents a challenge to this task commonly referred to as the 'curse of dimensionality' coined by Richard E. Bellman related to trying to handle this search in a tractable manner.

与许多大型医疗和人寿保险业务一样，Medavie Blue Cross已经开发了一个来自多个数据库来源的大型信息库，这些数据库来自其客户的多样性，如药房，医生办公室和诊所，并不断更新新的每月信息，用于其计费和索赔流程。可以对这些丰富的信息进行研究，以发现新的欺诈模式。然而，这种丰富多样的数据也对这项任务提出了挑战，通常被称为“维度灾难”，由理查德·E·贝尔曼试图以一种易驾驭的方式处理这次搜索。