SBIR Phase I: Secure and Scalable Collaboration Platform for Effective Detection of Money Laundering and Fraudulent Transactions

SBIR 第一阶段：用于有效检测洗钱和欺诈交易的安全且可扩展的协作平台

• 批准号: 2126901
• 负责人: Mohammad Sadegh Riazi
• 金额: $ 25.6万
• 依托单位: CIPHERMODE LABS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126901&HistoricalAwards=false
• 关键词: SBIR; Phase; Secure; Scalable; Collaboration

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to reduce financial fraud by closing the gap between current secure computation technologies and the technical requirements of modern anti-money laundering. Banks and financial institutions have a strong interest in creating a secure data exchange mechanism. In 2020 alone, the global fraud and anti-money laundering compliance costs exceeded $181 Billion. Audit and transaction monitoring accounted for 19% of this total expenditure. A single-digit percentage improvement in these two categories represents a market opportunity worth hundreds of millions of dollars. A major benefit of the proposed platform is that the complex financial dynamics of organized crime can be targeted and reduced. In addition to anti-money laundering and fraud detection, the secure computation framework generated by this proposal, i.e., a scalable and fast secure data exchange platform, can be applied to areas such as healthcare, the insurance industry, and national security.This Small Business Innovation Research Phase I project aims to create a systematic solution to overcome the current limitations in fighting fraudulent bank transactions. The main roadblock for effective detection of fraud and money laundering is the lack of a comprehensive view of client data. Currently, each bank has limited information about the client's financial activity and cannot benefit from a holistic view of the client’s profile in other banks. Naive solutions, such as creating a central data exchange entity, have been rendered impractical due to critical data security and privacy concerns. In this project, the team proposes a new methodology to address this challenge by leveraging advanced Secure Multiparty Computation (SMPC). Unlike anonymization approaches that hide a specific part of customers’ data, secure computation protocols guarantee data confidentiality even during a joint computation. In the past, SMPC has been impractical due to enormous computational and communication costs. However, the company has made both theoretical and practical breakthroughs that make SMPC more feasible for effective detection of fraud and money laundering. Further research and development is needed to make the solution truly applicable to real-world anti-money laundering scenarios, and thereby produce a technology that can drastically improve fraud detection.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响是通过缩小当前安全计算技术与现代反货币洗钱的技术要求之间的差距来减少金融摩擦。银行和金融机构对创建安全的数据交换机制有浓厚的兴趣。仅在2020年，全球欺诈和反洗钱的合规性成本就超过了1810亿美元。审计和交易监控占此总支出的19％。这两个类别的单位提高百分比是一个价值数亿美元的市场机会。拟议平台的一个主要好处是，可以将有组织犯罪的复杂财务动态作为目标和减少。除了反洗钱和欺诈探测外，该提案生成的安全计算框架（即可扩展且快速安全的数据交换平台）可以应用于医疗保健，保险业和国家安全等领域。本小型企业创新阶段I阶段I项目旨在建立一个系统的解决方案，以克服当前在欺诈性银行进行战斗的系统局限性。有效检测欺诈和洗钱的主要障碍是缺乏对客户数据的全面视图。当前，每家银行的信息有限，有关客户的财务活动，并且无法从其他银行中客户的个人资料中受益。由于关键的数据安全和隐私问题，诸如创建中央数据交换实体之类的天真解决方案（例如创建中央数据交换实体）已变得不切实际。在这个项目中，团队提出了一种新的方法，通过利用高级安全多方计算（SMPC）来应对这一挑战。与隐藏客户数据的特定部分的匿名方法不同，安全计算协议即使在联合计算过程中也可以保证数据机密性。过去，由于计算和通信成本的增强，SMPC一直不切实际。但是，该公司既实现了理论和实践突破，这使得SMPC对于有效检测欺诈和洗钱的可行性更为可行。需要进一步的研究和开发才能使该解决方案真正适用于现实世界中的反洗钱场景，从而产生一种可以极大地改善欺诈检测的技术。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来诚实地认为，通过评估来诚实地支持。