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

Award ID: 2126901

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 clients 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.

NSF Program Director: Peter Atherton