D-ISN: TRACK 2: Collaborative Research: Financial Network Disruptions in Illicit and Counterfeit Medicines (FIND-M)

D-ISN：轨道 2：合作研究：非法和假冒药品的金融网络中断 (FIND-M)

• 批准号: 2039946
• 负责人: Ioannis Kakadiaris
• 金额: $ 4万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039946&HistoricalAwards=false
• 关键词: ISN; TRACK; Collaborative; Research; Financial

Counterfeit and illegal drugs cause mortality and morbidity for millions of people around the world, as well as damage brands, undermine competition and the rule of law, cause economic losses and security threats, and corrupt financial systems. In light of the global coronavirus pandemic, there is an urgent need to develop a multipronged approach, including access to critical data, network analysis, distributed inference, identification of strategic points of intervention, and mitigation measures to disrupt the flow of counterfeit and illegal medicines in both high and low income countries. Identifying chokepoints (similar to other distribution networks) to effectively disrupt illegal medical supply chains is going to be an important feature of the project. If a solution to this challenge is not found, then prevention and enforcement successes will be partial, illegal entrepreneurs will adapt their modus operandi to circumvent controls, and public health, revenue, fair competition, justice, and security concerns will remain largely unaddressed. This Disrupting Operations of Illicit Supply Networks (D-ISN) planning has the potential to refine questions and solutions that can transform the national, state, and community-level discussions around illegal and counterfeit medicines. This collective effort will introduce a new governance and social control model whereby government, private sector, and academic parties are motivated to share skills, knowledge, and data to tackle the important social problems instigated by illicit entrepreneurs and criminal networks. The goal of this planning grant - bringing together stakeholders from academic, law-enforcement, public and private sectors - is to develop a distributed data infrastructure, populate this infrastructure, and conduct exploratory research in order to leverage financial, commercial and business data, along with previous best practices (from human trafficking and trade-based money laundering controls) for effective disruption of illegal medical and pharmaceutical supply chains. We aim to create robust approaches that will prevent or minimize the social harm caused by these illicit networks and we will coordinate novel, cutting edge efforts to improve outcomes for those victimized. Our specific objectives are to: 1) assemble the stakeholders and partners from other research communities to identify criminogenic asymmetries in the illicit supply networks of counterfeit and illegal drugs; 2) develop a task force, build out the infrastructure, and a detailed plan on how to mine distributed data (financial, business, commercial) using explainable machine learning methods to infer information needed to generate the multiplex networks; 3) stand up a task force, build multiplex networks that capture links discovered by mining the financial, business and commercial data, and develop a detailed research plan on how to discover the “weak-links”; 4) develop a task force, design mitigation strategies, and perform exploratory research on testing the products indicated by our analysis. By design, the project's hypotheses are broad at this stage, in order to incorporate inputs from the diverse stakeholders and partners, and to narrow down the focus during the planning stages of the project. Partners include representatives from trade, public health, and anti-counterfeiting teams both national and international. This research will be informed by the latest work in the area and specific scholars will be asked to join the academic team. Initially, the team will work with historical data, but there is a plan to work with several large financial institutions to run the algorithms we develop in a distributed, secure and privacy-preserving manner on current and live sources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

鉴于全球冠状病毒大流行，假冒和非法药品导致全世界数百万人死亡和发病，并损害品牌、破坏竞争和法治、造成经济损失和安全威胁以及腐败的金融体系。迫切需要制定多管齐下的方法，包括获取关键数据、网络分析、分布式推理、确定战略干预点以及缓解措施，以阻止假冒和非法药品在高收入和低收入国家的流动。识别瓶颈（与其他分销网络类似）有效破坏非法医疗供应链将是该项目的一个重要特征，如果找不到解决这一挑战的方案，那么预防和执法的成功将是局部的，非法企业家将调整他们的方式。规避管制的手段，以及公共卫生、收入、公平竞争、正义和安全问题在很大程度上仍未得到解决。这一破坏性非法供应网络 (D-ISN) 规划有可能完善问题和解决方案，从而改变国家的面貌。 、状态和这项集体努力将引入一种新的治理和社会控制模式，鼓励政府、私营部门和学术团体分享技能、知识和数据，以解决非法药物引发的重要社会问题。这项规划拨款的目标是将来自学术、执法、公共和私营部门的利益相关者聚集在一起，开发分布式数据基础设施，填充该基础设施，并进行探索性研究，以利用金融、商业。和业务数据，与之前的最佳做法（来自人口贩运和基于贸易的洗钱控制）一起，有效破坏非法医疗和药品供应链，我们的目标是制定强有力的方法，防止或尽量减少这些非法网络造成的社会危害。协调新颖、前沿的努力，以改善受害者的结果：1）召集其他研究界的利益相关者和合作伙伴，以识别假冒和非法药物的非法供应网络中的犯罪不对称性；2）制定一项任务；力量，建立基础设施，以及如何使用可解释的机器学习方法挖掘分布式数据（金融、商业、商业）以推断生成多路复用网络所需的信息的详细计划；3）成立一个工作组，建立多路复用网络；捕获通过挖掘财务、业务和商业数据发现的链接，并就如何发现“薄弱环节”制定详细的研究计划 4）建立一个工作组，设计缓解策略，并对测试产品进行探索性研究；由我们的分析表明。按照设计，该项目在此阶段的假设是广泛的，以便吸收不同利益相关者和合作伙伴的意见，并在项目规划阶段缩小重点范围，合作伙伴包括来自贸易、公共卫生和反腐败领域的代表。这项研究将参考该领域的最新工作，并将邀请特定学者加入学术团队。最初，该团队将使用历史数据，但有计划与多个团队合作。大型金融机构运行我们开发的算法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。