Money laundering is a problem no bank can solve alone. But working together is tricky, especially when it comes to sharing sensitive customer data.
At the 2019 Global AML and Financial Crime TechSprint, Partisia and partners including Sedicii, Goldman Sachs, and UBS demonstrated a new way forward. The proof of concept, called “Breaking Bad Actors,” used our advanced technology called Multi-Party Computation (MPC) to let banks compare inbound and outbound payments in real time, spotting mismatches and red flags without exposing private customer information.
By showing how MPC can be applied in practice, the project proved that it’s possible for banks to collaborate securely against financial crime, all while staying compliant with strict privacy laws.
Financial institutions are under growing pressure to step up their fight against money laundering, from regulators like the FCA, governments, and the public. Criminals are getting smarter, often moving money across different banks and countries to avoid detection.
To catch them, banks need to see the full picture. And that means working together.
But there’s a problem: Sharing customer data isn’t allowed.
Laws like the UK GDPR and the Data Protection Act protect people’s financial information, making it risky (and often illegal) for banks to share raw data with each other.
So while collaboration is key, privacy rules make it difficult.
The answer lies in a groundbreaking technology called Multi-Party Computation (MPC) that allows banks to work together on shared data without revealing any private information. Each bank’s data stays completely hidden, even as they run joint calculations to detect suspicious activity or spot patterns across institutions.
Here’s how it works in simple terms:
Each bank keeps its own data private
They work together to analyse it while encrypted
Only the final result is shared and visible, not the raw data
For example, banks can calculate a fraud risk score or flag suspicious activity without ever seeing each other’s customer details.
The big advantage? Banks can meet privacy regulations like GDPR while still getting the insights they need to fight money laundering.
When banks use Multi-Party Computation (MPC) to fight financial crime together, they unlock powerful advantages without putting sensitive data at risk. Here are five major benefits:
Each bank keeps its customer data private. Even during analysis, no one sees the raw data from anyone else. This protects both customers and institutions.
MPC is designed to meet strict privacy laws like the UK GDPR and the Data Protection Act. Banks can collaborate without worrying about legal or regulatory violations.
Money laundering often moves across institutions. MPC allows banks to spot patterns and suspicious behaviour that would be invisible in isolation.
With MPC, there's no central authority or outside organisation that sees all the data. Trust is built into the system, no single party has full control or access.
Because data is split into encrypted parts and shared across multiple systems, there’s no single point of failure. Even if one part is compromised, the data stays safe.
Here’s how a more secure and collaborative approach to anti-money laundering could work across UK banks using MPC:
Real-time AML detection
Banks use MPC to securely analyse transaction patterns across institutions, helping to detect money laundering that spans multiple banks.
Joint risk scoring
Banks combine selected data signals to calculate risk scores for customers or transactions without revealing any underlying personal data.
Easier regulatory reporting
MPC outputs can be used for compliance reporting, offering transparency and insight without breaching privacy or GDPR rules.
Breaking down data silos
Instead of working in isolation, banks can contribute to a shared analysis, gaining access to better insights while keeping all private data protected.
With MPC, multiple parties can compute on shared data without seeing each other’s inputs. It’s a paradigm shift for data privacy and compliance.
Criminals aren’t working alone, they move money across different banks, countries, and systems. To keep up, banks need to work together too.
With the right tools, they can do it securely and within the law.
MPC and blockchain make this possible. These technologies let banks share insights and spot financial crime without exposing customer data or breaking privacy rules. And this isn’t just theory. It’s already been used successfully in real-world cases across sectors like agriculture, healthcare, and digital identity.
Now is the time for banks to take the next step and start collaborating safely.
At Partisia, we believe that secure collaboration is key to stopping financial crime and it shouldn’t come at the cost of privacy.
For too long, banks have faced a false choice: either work together and risk exposing sensitive data, or stay compliant and work in isolation. With our technology, Multi-Party Computation (MPC) and blockchain, that trade-off is no longer necessary.
Our technology makes it possible for banks to:
Detect cross-bank money laundering patterns without sharing customer data
Run joint risk scoring models while keeping internal systems and inputs private
Feed results directly into compliance tools without breaching GDPR or other regulations
Build trust between institutions without relying on a central third party
We’re ready to help UK banks adopt this privacy-first approach to AML and unlock the value of collaboration, safely and securely.
Partisia has already shown what’s possible with our “Breaking Bad Actors” proof of concept at the 2019 Global AML and Financial Crime TechSprint, developed together with Sedicii, Goldman Sachs, Ex Ante Advisory, UBS, and Deloitte. That experience proves that privacy doesn’t have to slow innovation. And with our platform, it won’t.
Looking to explore how privacy-first technologies can strengthen your AML efforts, support compliance, or power new digital wallet solutions?
Get in touch with our team to learn how Partisia’s platform can help you collaborate securely while staying fully compliant.