{"id":11213,"date":"2025-10-02T13:22:54","date_gmt":"2025-10-02T13:22:54","guid":{"rendered":"https:\/\/fintech.global\/regtech100\/?p=11213"},"modified":"2025-10-02T13:22:54","modified_gmt":"2025-10-02T13:22:54","slug":"can-federated-intelligence-solve-the-privacy-problem-in-compliance","status":"publish","type":"post","link":"https:\/\/fintech.global\/regtech100\/can-federated-intelligence-solve-the-privacy-problem-in-compliance\/","title":{"rendered":"Can federated intelligence solve the privacy problem in compliance?"},"content":{"rendered":"

In an era where data fuels innovation but privacy regulations like GDPR and CCPA loom large, organizations grapple with a paradox: harnessing collective intelligence without compromising sensitive information. Enter federated intelligence\u2014a decentralized approach to AI that trains models across distributed datasets without ever centralizing raw data, potentially revolutionizing compliance landscapes. But can it truly bridge the gap between robust analytics and ironclad privacy?<\/strong><\/p>\n

According to RegTech firm\u00a0Salv<\/a>, when attempting to define the idea of federated intelligence, you can think of it on a spectrum.<\/p>\n

\u201cOn one end, you\u2019ve got data sharing based on customer consent \u2014 for example, if I open a new account and tell the bank to pull all my KYC documents from my existing institution. That\u2019s entirely permission-based. It\u2019s not suspicious, it\u2019s not about crime \u2014 it\u2019s just a customer-authorised data transfer,\u201d the firm said.<\/p>\n

\u201cOn the other end of the spectrum is what Salv Bridge enables: data sharing based on strong suspicion. If there\u2019s evidence of fraud, money laundering, or a sanctions hit, that creates a legal basis to share information without customer consent. There\u2019s a documented trigger, a specific purpose, and an audit trail.<\/p>\n

\u201cFederated intelligence sits somewhere in the middle. There isn\u2019t yet strong suspicion. It\u2019s not about good behaviour either. You might see something that seems off \u2014 something that could become a fraud case down the line \u2014 but you\u2019re not ready to open an investigation.\u201d<\/p>\n

In the view of Salv, this is where federated intelligence becomes useful. It enables institutions to share behavioural insights or detection logic, without exposing personal data. This, Salv claims, is the principle behind its own federated learning work.<\/p>\n

Work in practice<\/strong><\/p>\n

\u00a0<\/strong>The Estonian RegTech firm suggested the clearest example of how federated intelligence works in practice is Salv\u2019s Monitoring Rule Library.<\/p>\n

The firm said, \u201cOur customers \u2014 financial institutions \u2014 can create rules that describe suspicious behaviour. For example: if a customer receives funds from a high-risk country, followed by a rapid outbound transfer. These are not full investigations. There\u2019s no sensitive data. They\u2019re just detection patterns that help other institutions improve their own monitoring.\u201d<\/p>\n

These rules can then be shared \u2013 with permission \u2013 through the library. \u201cOther institutions can adapt them, use them, or improve them. They can even feed them back into the community with updated parameters. It becomes a kind of open-source AML.<\/p>\n

\u201cAnd soon, we\u2019re making that process even more powerful. In our upcoming roadmap, we\u2019ll be adding AI-powered pattern suggestions that help analysts spot new suspicious behaviours based on shared rule logic. It\u2019s a way to crowdsource insight \u2014 without ever sharing data.\u201d\u00a0<\/strong><\/p>\n

The biggest risks<\/strong><\/p>\n

For Salv, when it comes to the biggest risks of federated intelligence, there are two common misconceptions. The first is that federated intelligence requires a new or exotic legal basis. It doesn\u2019t. \u201cIf you\u2019re not sharing personal data \u2014 only logic \u2014 then you\u2019re not bound by the same constraints,\u201d said Salv.<\/p>\n

The second is that you need complex infrastructure or cryptographic techniques to make it work. You don\u2019t. \u201cIf the right controls are in place, regular encryption works just as well as multi-party computation. It\u2019s not about the tech \u2014 it\u2019s about the purpose, the process, and the legal foundation,\u201d Salv explained.<\/p>\n

Emerging forms\u00a0<\/strong>\u00a0<\/strong><\/p>\n

A key question to ask is whether other forms of federated intelligence are emerging. Here, Salv agrees \u2013 and one example they suggest is entity-based scoring.<\/p>\n

\u201cSome companies use a model where institutions submit partial suspicion indicators. So if Bank A says, \u201cWe\u2019re seeing odd behaviour from this person,\u201d and Bank B independently reports the same thing, the system increases that entity\u2019s risk score \u2014 say to minus two or minus five,\u201d said Salv.<\/p>\n

The idea, Salv suggests, is that the bank doesn\u2019t see who submitted what \u2013 they just see that other institutions have raised concerns. Salv claims it is similar to suspicious entity sharing, in that it lets you see whether someone is raising red flags elsewhere in the system.<\/p>\n

\u201cThat model can be useful for flagging cases that wouldn\u2019t be visible in isolation, especially when it\u2019s fed back into monitoring or onboarding processes,\u201d suggests the company.<\/p>\n

Regulatory and technical hurdles<\/strong><\/p>\n

Which regulatory or technical hurdles must federated intelligence overcome? For Salv, the most vital place to start is with the legal basis \u2013 not the technology.<\/p>\n

\u201cYou can share data through encrypted messaging, multi-party computation, or even paper and envelopes \u2014 it doesn\u2019t matter. If the underlying sharing isn\u2019t lawful, the tech can\u2019t make it lawful,\u201d said the firm.<\/p>\n

For Salv, there are three key areas that matter. The first is purpose \u2013 why they are sharing, what is the business value and does it help reduce crime. Secondly, security \u2013 is the data protected against external threats? Finally, privacy, and whether a firm is respecting data protection laws and bank secrecy.<\/p>\n

\u201cIf you have all three in place, federated intelligence becomes not just feasible \u2014 but essential,\u201d Salv concluded.<\/p>\n

Read the daily RegTech news<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

In an era where data fuels innovation but privacy regulations like GDPR and CCPA loom large, organizations grapple with a paradox: harnessing collective intelligence without compromising sensitive information. Enter federated intelligence\u2014a decentralized approach to AI that trains models across distributed datasets without ever centralizing raw data, potentially revolutionizing compliance landscapes. But can it truly bridge […]<\/p>\n","protected":false},"author":4,"featured_media":11214,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"yoast_head":"\nCan federated intelligence solve the privacy problem in compliance? - RegTech100<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fintech.global\/regtech100\/can-federated-intelligence-solve-the-privacy-problem-in-compliance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can federated intelligence solve the privacy problem in compliance? - RegTech100\" \/>\n<meta property=\"og:description\" content=\"In an era where data fuels innovation but privacy regulations like GDPR and CCPA loom large, organizations grapple with a paradox: harnessing collective intelligence without compromising sensitive information. 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