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Automated Analysis of Furlough Workers to Minimise Fruadulent Applications on behalf of HMRC

Automated Analysis of Furlough Workers to Minimise Fruadulent Applications on behalf of HMRC
代表 HMRC 对休假工人进行自动分析,以最大程度地减少欺诈性申请
批准号:
58065
负责人:
金额:
$6.37万
依托单位国家:
英国
项目类别:
Feasibility Studies
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

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中文摘要
翻译
Synalogik将通过其创新技术的部署,在新冠肺炎疫情引发的国家危机期间为英国税务和海关总署及其四面楚歌的工作人员提供帮助。Synalogik将通过与自动化过程相结合的研发来改造在线冠状病毒工作保留计划(CJRS)验证系统,这将极大地改进已实施的人工索赔人核实检查程序和做法。目前的做法是劳动密集型的;需要人工程序来核实新的索赔人,这需要很长的时间来核实。目前的新冠肺炎索赔流程将与标准完全不同,由于系统超载,可能无法进行全面核实。HMRC不是为处理如此大量的新数据而设计的,因此可能无法应对这些极端情况。部署基于云的技术功能将大大减少宝贵的资源并节省大量时间,同时执行必要的验证检查,从而帮助HMRC调查人员和分析人员。此外,名为“Scout”的先进技术可以识别那些与其应用程序相关的风险评估/指标,这些应用程序导致防止个人或有组织犯罪集团(OCG)故意针对CJRS计划提出欺诈性申请,而HMRC则面临前所未有的压力,因为由于混乱,在几周内寻求的申请被低估。Synalogik是一家总部位于英国的科技公司,由数据科学、情报、安全和欺诈调查专家创立。Synalogik开发了一种独特的解决方案,在革命性情报管理的尖端,自动识别、检测和证明个人或OCG的欺诈性金融活动。Platform-Scout显著提高了组织的效率、能力和能力,例如HMRC,这些组织依赖信息在这种情况下做出智能和基于证据的人员身份验证决策,这些情况发生在整个新冠肺炎期间无与伦比的CJRS应用到HMRC期间。核心到Scout是数据分析的自动化,它根据需要从不同的数据源提取情报,以当前方法的一小部分时间和成本为HMRC提供可行的情报。这个开发项目将专注于通过研发、集成、纳入特定案例管理系统和测试增加的平台功能来增强这项技术的能力。具体地说,是在多个英国数据集上运行的技术,以更好地预测和识别整个税收部门的欺诈行为。
英文摘要
Synalogik through the deployment of its innovative technology will provide assistance to Her Majesty's Revenue and Customs (HMRC) and its beleaguered Staff during the period of national crisis concerning the Covid-19 pandemic. Synalogik will transform the on-line Coronavirus Job Retention Scheme (CJRS) validation system through R&D concerning the integration of an automated process which considerably improves the implemented manual claimant verification check procedures and practices. The current approach is labour intensive; manual processes are required to verify new claimants which require extensive time to validate. The current Covid-19 claims processes will be entirely different from the norm and potentially incapable of full verification due to the overloaded system. HMRC is not designed to cope with such an amount of new data and therefore is potentially ill-equipped for these extreme circumstances.Deploying a Cloud-based technology capability will assist HMRC investigators and analysts by significantly reducing valuable resources and saving significant time whilst conducting the necessary verification checks. Additionally, the advanced technology named 'Scout' identifies those claims which had risk assessment/indicators associated to their applications which gave rise to preventing fraudulent applications by individuals or organised crime groups (OCGs) targeting the CJRS scheme deliberately, whilst HMRC are under unprecedented pressure due to the underestimated applications sought within a few weeks due to the pandemic.Synalogik are a UK based technology company, founded by experts in data science, intelligence, security and fraud investigation.Synalogik has developed a unique solution, at the cutting edge of revolutionary intelligence management, to automate the identification, detection and evidencing of fraudulent financial activity by individuals or OCGs. The Platform - Scout substantially increases the efficiency, capability and capacity of organisations, such as HMRC, who rely on information to make intelligent and evidence-based personnel authentication decisions during such circumstances experienced during the unparalleled CJRS applications to HMRC throughout the Covid-19 pandemic.Core to Scout is the automation of data analysis that draws intelligence on-demand from disparate data sources, providing HMRC with actionable intelligence in a fraction of the time, and at a fraction of the cost of current approaches.This development project will focus on increasing the capabilities of this technology, through R&D, integration expansion, incorporation of specific case management systems and testing of increased Platform functionality, specifically technology operating over multiple UK datasets to better predict and identify fraud across the Revenue/Taxes sector.
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  • 批准号:
    --
  • 项目类别:
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  • 资助金额:
    --
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  • 批准年份:
    2016
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    赵爱琴
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大规模微阵列数据组的meta-analysis方法研究
  • 批准号:
    31100958
  • 项目类别:
    青年科学基金项目
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  • 批准年份:
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