Quantum computing’s promise to fight financial crime is getting a regulator-led test.
The Financial Conduct Authority (FCA) said Nov. 5 it will launch a Quantum Experimentation Group (QEG) in the first quarter of 2027, bringing together financial services firms, quantum technology providers, and national research laboratories. Its first use case is quantum machine learning for detecting money laundering and fraud, using an FCA synthetic dataset.
The FCA calls the QEG the world’s first regulator-led quantum computing experimentation group. The announcement came a day after the FCA urged firms to prepare for post-quantum cryptography (PQC), highlighting quantum computing’s potential applications alongside its security risks.
Who Can Join the FCA’s Quantum Group
The FCA is inviting expressions of interest from three types of UK organizations, according to its program page:
- Financial services firms: From those just exploring quantum to those already experimenting.
- Quantum computing providers: Companies offering expertise, computing resources, and applications engineering.
- Research labs: Institutions that can support experiment design and evaluation.
Participants will work on an FCA-defined problem, using the synthetic dataset to compare quantum approaches against classical techniques. Participation is not FCA endorsement, and registering does not guarantee a place. Registration closes Dec. 31, 2026.
“By bringing together firms, technology providers and researchers we can build readiness and identify practical applications for common industry challenges, starting with financial crime,” said Jessica Rusu, chief data, information and intelligence officer at the FCA.
Opportunity and Threat in the Same Week
On Nov. 4, Mark Francis, the FCA’s director of specialists, urged firms to act now on PQC migration. He wrote that failed change projects accounted for around a fifth of incidents reported to the FCA in 2025.
| Date | FCA action | What it asks of firms |
|---|---|---|
| Nov. 4, 2026 | PQC blog post | Start migration work now |
| Nov. 5, 2026 | QEG announced | Register interest by Dec. 31, 2026 |
| Q1 2027 | QEG launches | Test quantum machine learning on synthetic fraud and money laundering data |
| 2028, 2031, 2035 | National Cyber Security Centre (NCSC) milestones cited by the FCA | Complete a migration plan by 2028, carry out high-priority migration activities by 2031, and complete migration across all systems, services, and products by 2035 |
Preparation gaps also extend beyond the UK. A Government Accountability Office (GAO) audit found that none of 24 U.S. federal agencies had fully addressed all three assessed preparation practices for PQC migration.
Why Synthetic Data Matters
The FCA says suitable data remains a practical barrier to quantum experimentation, since banks cannot freely share customer transaction records.
In April 2026, the FCA published research on a synthetic anti-money laundering dataset built with the Alan Turing Institute, Plenitude Consulting, and Napier AI. The QEG announcement and program page do not identify whether participants will use that earlier dataset.
Gartner expects banking, finance, and insurance to overtake the public sector in quantum spending in 2028, naming fraud detection among the uses.
What eWeek Found: The Evidence Banks Should Watch
The QEG’s most useful feature is its shared starting point: an FCA-defined financial crime problem and a common synthetic dataset. That gives participants a basis for comparing quantum approaches with classical techniques. It does not establish that quantum methods perform better, or guarantee that different participants’ results will be directly comparable.
Benchmark selection matters. ORCA’s PT-3 announcement, for example, highlighted a scheduling speedup over its previous-generation system. Buyers still need comparisons with strong classical alternatives to judge whether a quantum approach offers a practical benefit.
For banks evaluating results from the QEG or a vendor pilot, the useful questions include:
- Detection: Does the method identify more suspicious activity at a comparable false-positive rate?
- Speed: Does it improve the time needed to produce useful results, including data preparation and supporting classical computation?
- Cost: Does any improvement justify the full cost of hardware access, integration, and specialist support?
- Real-world performance: Do results on synthetic data hold up under actual banking conditions?
The FCA has committed to sharing high-level learning while protecting participant-specific and commercially sensitive information. How much detail it publishes about evaluation methods and results will determine how useful those findings are to banks outside the group.
For financial services leaders, experimentation and security preparation require separate decisions. A promising fraud-detection result could justify further testing; PQC migration planning already has milestones. Firms should assess each on its own evidence, resources, and timetable.
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