Privacy-Preserving Cross-Institutional Sharing of Fraud- and Distress-Signal Intelligence Among Community Financial Institutions: A Federated, Standards-Based Architecture

Abstract

Community financial institutions, banks, credit unions, and Community Development Financial Institutions (CDFIs) alike, remain structurally disadvantaged in detecting cross-institutional fraud and financial-distress patterns: FinCEN's Section 314(b) voluntary information-sharing programme, the primary existing legal mechanism for such sharing, shows only approximately 12.3 percent overall institutional participation two decades after its creation [8]. This article proposes a privacy-preserving, federated architecture for cross-institutional fraud- and distress-signal intelligence sharing specifically designed for the community financial institution and CDFI sector, drawing on published, cited technical evidence including a peer-reviewed federated meta-learning framework that outperformed ten baseline models for credit card fraud detection [3], and a peer-reviewed federated architecture combining differential privacy with secure multi-party computation for financial applications [7]. The article situates this architecture specifically within the CDFI sector's existing collaborative infrastructure, including Opportunity Finance Network, which connects nearly 500 member CDFIs and has facilitated 124 billion U.S. dollars in cumulative financing through 2023 [9], and addresses the distinct challenge that CDFI relationship lending depends on "soft" qualitative borrower information poorly suited to fully automated data-sharing, a limitation this article addresses directly rather than assumes away.