Privacy-Enhancing Collaboration in BFSI
Today, BFSI institutions are expected to act on data while complying with strict data protection laws (e.g., GDPR, data residency statutes). But regulatory and structural barriers make inter-organizational collaboration difficult. As a result, fraud goes undetected, credit is under- or over-extended, and risk decisions are made on incomplete information. Institutions often say, “We are only seeing 25% of our customers’ banking activity… the majority happens outside our walls” (Oracle).
MPC solves this by letting institutions collaborate without sharing sensitive data. It allows computations over encrypted or secret-shared data inputs, where no party can learn the others’ inputs—only the result. Privacy-enhancing technologies (PETs) like MPC are now being promoted by regulators (e.g., UK-US PETs Challenge, FATF) as a tool to both enhance data collaboration and uphold data rights (NIST).
Algemetric’s implementation overcomes the main blockers seen in early-stage MPC—namely, performance, decimal accuracy, and integration complexity. Our Mercury MPC engine and PIE encoding technology support financial-grade precision and seamless deployment.
