Fraud investigation at scale: Methods and tools

大规模欺诈调查：方法和工具

• 批准号: 494291-2016
• 负责人: Cao, Jiguo
• 金额: $ 12.02万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Strategic Projects - Group
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=646809
• 关键词: Fraud; investigation; scale; Methods; tools

Fraud detection is critical for many organizations, such as insurance companies, financial institutions, governments, police, law enforcement units, and retail companies. Typically, after a fraud detection system returns suspects human investigators have to examine those suspects manually. More often than not, investigating a fraud suspect can be very costly. A fraud investigator as a decision maker has to strive for accurate decisions that are effective for business. we propose a focusing research program to tackle the next strategically important challenge in fraud detection: how can we take a systematic data science approach to support human investigators to investigate fraud suspects effectively?**This project aims at producing new methods and tools for organizations so that better decisions can be made in fraud investigation to support the business of those organizations. For example, investigating similar suspects together may help to not only lower down the average cost of investigation, but also improve the investigation quality due to more available data and knowledge learned and transferable among similar cases. Investigations can also be planned better so that the gain from the recovered cases can be maximized under various constraints in business, such as within a budget on investigation cost and the amount of extra interaction with customers.**This project directly addresses the target area Information and Communications Technologies (ICT), and focuses on the research topic Advanced Data Management and Analytics. Particularly, the project is wholly about Analytics for decision-making. The core theme of the project is to make better decisions in fraud investigation in the context of heterogeneous data at scale, complex business objectives and practical constraints in business. This project closely connects with well engaged collaborators crossing multiple industry segments. It integrates fundamental research, data science tool building, application prototypes and case studies. It also contains a significant HQP training component.********

欺诈检测对于许多组织至关重要，例如保险公司、金融机构、政府、警察、执法单位和零售公司。通常，在欺诈检测系统返回嫌疑人之后，人类调查人员必须手动检查这些嫌疑人。 通常情况下，调查欺诈嫌疑人的成本可能非常高。作为决策者，欺诈调查员必须努力做出对业务有效的准确决策。我们提出了一个重点研究计划，以应对欺诈检测中下一个具有战略意义的挑战：我们如何采取系统的数据科学方法来支持人类调查人员有效地调查欺诈嫌疑人？该项目旨在为各组织提供新的方法和工具，以便在欺诈调查中作出更好的决定，以支持这些组织的业务。 例如，将类似的嫌疑人集中调查，不仅有助于降低调查的平均成本，而且由于在类似案件中学到和可转让的更多可用数据和知识，也有助于提高调查质量。 调查工作也可以更好地规划，以便在各种业务限制下，例如在调查费用预算和与客户的额外互动量内，从追回的案件中获得最大收益。该项目直接涉及目标领域信息和通信技术（ICT），并侧重于研究主题高级数据管理和分析。 特别是，该项目完全是关于决策分析的。该项目的核心主题是在大规模异构数据、复杂的业务目标和业务实际限制的背景下，在欺诈调查中做出更好的决策。该项目与跨多个行业领域的合作者紧密联系。它集成了基础研究，数据科学工具构建，应用原型和案例研究。 它还包含一个重要的HQP培训部分。*