In my previous blog, "Smarter Together: How Data Sharing Will Transform Financial Services" (https://bankloch.blogspot.com/2026/01/smarter-together-how-data-sharing-will.html), I described how the financial sector has enormous untapped value in cross-institution collaboration. Fraud detection, KYC and AML, credit intelligence, Verification of Payee, smarter payment routing… the potential is massive.
But there is a hard truth: data sharing only works if privacy works. And privacy in financial services operates on two very different levels.
The First Level: Customer Control & Trust
Customers do not want their financial lives circulating across institutions without explicit control.
Even when they give consent, they expect:
The right to revoke it
The right to be forgotten
Full transparency on who accesses their data
Clear purpose limitation
This is not just about complying with GDPR. It is about trust. And trust, once lost, is almost impossible to rebuild.
The Second Level: Security & Third-Party Risk
Even if customers agree to data sharing, institutions still face another challenge: Every data exchange increases the attack surface, i.e.
More endpoints
More integrations
More third parties
More contractual dependencies
You can perform due diligence. You can demand audits and certifications. You can ensure contractual safeguards are in place.
But if a breach happens, the reputational damage is still yours.
That is the uncomfortable reality of modern financial ecosystems.
Enter Privacy-Enhancing Technologies (PETs)
Privacy-Enhancing Technologies (PETs) allow institutions to collaborate without exposing raw data.
They do not eliminate risk, but they significantly reduce dependency on the security posture of external parties, meaning you stay in control.
PETs are not a single solution, but a toolbox of complementary technologies that can be used individually or combined, depending on the use case. For example:
Secure Multi-Party Computation (MPC): Multiple institutions compute a shared result without revealing their individual datasets. For instance, banks detecting mule accounts across institutions without sharing full customer databases.
Homomorphic Encryption: Enables computation directly on encrypted data. For example, a bank sends encrypted transaction data to a provider, which calculates a risk score and returns it in encrypted form, only the bank can decrypt the result.
Federated Learning: Models are trained across institutions without centralizing data. Each bank trains locally, and only model updates are shared. Ideal for joint fraud detection or credit modeling.
Trusted Execution Environments (TEEs): Secure hardware enclaves where data is decrypted and processed in isolation. Even system administrators cannot access the data inside the enclave. Suitable for joint platforms and controlled collaboration hubs.
Differential Privacy: Adds mathematical “noise” to outputs so individuals cannot be identified. Particularly useful for regulatory reporting and benchmarking.
Zero-Knowledge Proofs (ZKPs): Allow you to prove something without revealing the underlying data. For example, proving a customer passed KYC without sharing identity documents, or proving compliance without exposing transaction details.
Synthetic data: Artificially generated datasets that statistically resemble real data. Useful for software testing, model development, and experimentation when real data cannot be shared.
Data Masking & Tokenization: Replace sensitive fields with tokens.
Pseudonymization: Replace direct identifiers with reversible mappings.
Anonymization: Irreversibly remove identifiers to prevent re-identification.
Each use case requires a tailored approach.
Cross-bank fraud and AML may combine MPC, Federated Learning, and TEEs.
Regulatory reporting may combine Differential Privacy and ZKPs.
Most real-world architectures will use multiple PETs together.
The real value of PETs is not purely technical, it is strategic. PETs allow financial institutions to:
Unlock ecosystem value
Reduce third-party dependency risk
Strengthen customer trust
Align with regulation by design
If data sharing is the future of financial services, then PETs are the infrastructure that makes that future viable.
Without them, collaboration will remain limited by fear.
With them, we can truly be smarter together, without compromising trust.
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