Can Synthetic Data Solve Your AML Training Problem?
Synthetic Data for KYC and AML: Promise, Limits, and What Regulators Will Ask
When labelled SAR and KYC data is scarce, synthetic generation is appealing. But synthetic risk profiles cannot replicate the behavioural subtlety of real cases — and regulators are taking a careful look.
Why Synthetic Data Has Become Attractive
Financial institutions face a genuine data problem. Confirmed Suspicious Activity Reports (SARs) and labelled KYC cases are among the most sensitive records a bank holds — restricted by privacy regulation, siloed across jurisdictions, and rarely available in the volumes that modern machine learning demands. The result is chronically imbalanced training sets, where fraud and laundering events represent a tiny fraction of observed transactions.
Synthetic data offers an escape route of sorts. Organisations including JP Morgan, IBM, and SWIFT have already produced synthetic AML datasets for model development purposes, with the goal of addressing both privacy concerns and class imbalance — the problem where money laundering transactions occur far less frequently than legitimate ones, making detection models hard to train reliably.
The appeal is clear: privacy-safe generation, easier sharing across teams and vendors, and the ability to stress-test models against typologies that may be under-represented in historical records. Source: arxiv.org — Hybrid Data for AML Models, 2024
What Synthetic Data Can Genuinely Solve
- Augmenting rare event classes (fraud, SAR-linked transactions) in training sets
- Sandbox and sprint environments — safe experimentation without real customer exposure
- Testing model explainability: known inputs produce traceable outputs
- Simulating emerging typologies not yet well-represented in historical data
- Enabling cross-institution collaboration without raw data sharing
- Replicating the behavioural subtlety and evolution of real laundering networks
- Capturing novel or emerging schemes the generator has never seen
- Providing the provenance trail regulators require for model validation
- Replacing real-data holdout sets for production model sign-off
- Evidencing real-world typology linkage for audit review
On the explainability point, there is a genuine upside. Because synthetic datasets are built with controlled parameters, they make it easier to understand why a model responds in a particular way — an advantage when demonstrating alignment with regulatory expectations around transparency and model governance.
The FCA's Position: Experimentation, Not Substitution
The UK's Financial Conduct Authority has been the most active regulator in this space. In 2025, the FCA published a Research Note documenting a multi-stakeholder initiative with the Alan Turing Institute, Plenitude Consulting, and Napier AI to produce a fully synthetic, privacy-preserving dataset embedded with realistic money laundering typologies. That dataset is being made available to firms participating in an AML Solution Sprint through the FCA Digital Sandbox.
The FCA has also developed an in-house synthetic data tool for sanctions screening testing — used to probe firms' governance, vendor oversight, and false positive rates without requiring access to live customer records. In a 2023 speech, the regulator noted the tool had revealed significant control weaknesses at some firms.
The FCA's message is consistent: synthetic data is a tool for experimentation, not a basis for stepping back from rigorous operational oversight. Firms must continue to ground their AML systems in real-world testing and validation. No major regulator has yet endorsed synthetic data as acceptable for AML model validation or regulatory reporting.
Sources: TLT LLP analysis of FCA Synthetic Data and AML Project Report, May 2026; A-Team Insight on FCA AI Update 2025; FCA Synthetic Data Governance Guidance (fca.org.uk)
What Regulators Will Ask
Whether you are under FCA scrutiny in the UK, EBA oversight in the EU under the new AMLR framework, or FinCEN's model risk management expectations in the US, the questions are converging. Expect supervisors to probe the following:
Regulatory Scrutiny Checklist
- Provenance and labelling: Can you demonstrate exactly how the synthetic dataset was generated, what parameters were set, and what typologies were embedded? Datasets must be clearly labelled as synthetic and segregated from production data.
- Real-data validation: Has every model trained on synthetic data been validated against a real-world holdout set before deployment? This is a baseline expectation under OCC model risk management guidance and EBA guidance on advanced analytics in AML/CTF.
- Bias and overfitting evidence: Synthetic datasets can reinforce existing model biases or produce false correlations. Regulators will ask for bias assessment documentation conducted post-generation.
- Typology linkage: Can you trace the embedded laundering patterns in your synthetic data to recognised FATF or national typologies? Unmoored scenarios do not satisfy audit requirements.
- Governance trail: Were model risk management and internal audit involved before synthetic data was used in any training pipeline that feeds a production system?
Sources: AML Partners — Synthetic Data in AML, 2025; EBA guidance on advanced analytics in AML/CTF; OCC model risk management guidance (SR 11-7)
The Hybrid Path Forward
The emerging consensus in compliance and RegTech circles is a hybrid approach: use synthetic data to augment real datasets, not replace them. Techniques such as federated learning — where institutions train models collaboratively without sharing raw data — offer a complementary route that keeps real transactional signal in the loop while addressing privacy constraints.
For firms in experimentation mode, the practical governance steps are well established: label synthetic datasets explicitly, restrict their use to sandboxes, validate all outputs against real holdout data, document creation logic and limitations thoroughly, and engage regulators early about methodology and intent. These controls mirror traditional model risk management expectations — because synthetic data introduces model risk of its own.
Synthetic data has a legitimate and growing role in financial crime compliance — but it is a research and development asset, not a shortcut past the hard work of acquiring, labelling, and governing real case data. The firms that will navigate regulatory scrutiny successfully are those that treat synthetic generation as one layer in a well-governed data strategy, not as a solution to the underlying data problem itself.
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