The Inpher team (“Secret Computers”) implemented a secure multiparty computation protocol at the Tech Sprint in order to effectively identify suspicious activity within financial transaction data. Image from FCA.

The Inpher team (“Secret Computers”) implemented a secure multiparty computation protocol at the Tech Sprint in order to effectively identify suspicious activity within financial transaction data. Image from FCA.

Last week, Inpher took part in the Financial Conduct Authority (FCA) 2019 Global AML and Financial Crime TechSprint held in London. The purpose of the TechSprint was to determine how privacy-enhancing technologies (PETs) can effectively combat financial crime, detect fraudulent activities, and prevent money laundering within the financial services industry. Inpher was invited by the FCA to apply its expertise in PETs (specifically, secure multiparty computation) to problems in fraud detection and anti-money laundering (AML).

According to a 2018 report by Refinitiv, financial institutions lose an estimated $1.45 trillion annually from financial crimes while spending $1.28 trillion to prevent them in the first place. Regulators are keen to find new solutions to these old-age problems that have dragged the industry for years. In a recent speech, the Head of FCA’s Financial Crime Department, Rob Gruppetta, asserted that “data analytics and machine learning are widely seen as the approaches with the greatest potential to improve current practices, particularly in the field of transaction monitoring.” Specifically, over the past year, the FCA has focused on “the importance of data and knowledge sharing among relevant bodies in identifying and impeding complex criminal networks.” Transaction monitoring data is critical for fraud detection and anti-money laundering efforts, but it is too frequently “siloed within institutions, resulting in a global problem being tackled at an individual firm level.

A central question raised at the TechSprint: How can knowledge sharing between financial institutions and regulators occur while adhering to strict data privacy legislation and requirements?

The answer: Inpher's XOR Secret Computing Engine utilizing secure multiparty computation.

The team, led by CTO & Cofounder Dr. Dimitar Jetchev, used Inpher’s XOR Secret Computing Engine deployed on Google Cloud Platform to apply a secure multiparty computation protocol to disparate data sources, extracting credible suspicions while keeping the data sovereign in a GDPR-compliant manner. Inpher and its collaborators from Standard Chartered Bank, Goldman Sachs, and PwC won the TechSprint’s People’s Choice Award after its solution was evaluated for market readiness, effectiveness, and creativity.

If you’re interested in learning more about Inpher’s solution at the FCA TechSprint or how Secret Computing can solve your data privacy concerns, reach out to us at info@inpher.io.