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.

假冒和非法药物导致世界各地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，并腐败金融体系。鉴于全球冠状病毒大流行，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，以及采取缓解措施，以阻断假冒和非法药物在高收入和低收入国家的流动。确定瓶颈（类似于其他分销网络），以有效地破坏非法医药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办法，那么预防和执法的成功将是局部的，非法企业家将调整其作案手法以规避管制，公共卫生、收入、公平竞争、司法和安全问题将在很大程度上得不到解决。这种破坏非法供应网络（D-ISN）的计划有可能改进问题和解决方案，可以改变国家，州和社区层面关于非法和假冒药品的讨论。这一集体努力将引入一种新的治理和社会控制模式，促使政府、私营部门和学术界分享技能、知识和数据，以解决非法企业家和犯罪网络引发的重要社会问题。这项规划拨款的目标-汇集了来自学术，执法，公共和私营部门的利益相关者-是开发分布式数据基础设施，填充此基础设施，并进行探索性研究，以利用金融，商业和商业数据，沿着以前的最佳做法（从人口贩运和基于贸易的洗钱控制），以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，以防止或尽量减少这些非法网络造成的社会危害，我们将协调新颖的，尖端的努力，以改善受害者的结果。我们的具体目标是：1）聚集来自其他研究界的利益相关者和合作伙伴，以确定假冒和非法药物非法供应网络中的犯罪不对称性; 2）建立一个工作组，建立基础设施，并制定详细的计划，如何挖掘分布式数据（金融、商业、商业）使用可解释的机器学习方法来推断生成多路网络所需的信息;（3）成立专责小组，建立多元网络，捕捉金融、商业和商业数据挖掘中发现的环节，并就如何发现"薄弱环节"制定详细的研究计划;（4）成立专责小组，设计缓解策略，并就测试我们的分析所显示的产品进行探索性研究。按照设计，该项目的假设在本阶段是宽泛的，以便纳入不同利益攸关方和合作伙伴的投入，并在项目规划阶段缩小重点。合作伙伴包括来自国家和国际贸易、公共卫生和反假冒团队的代表。这项研究将由该领域的最新工作提供信息，并邀请特定学者加入学术团队。最初，该团队将使用历史数据，但计划与几家大型金融机构合作，以分布式、安全和隐私保护的方式在当前和实时源上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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。