Why Synthetic Data in Financial Services Matters
AI in financial services needs data, and the most useful data is also the most sensitive. Customer transactions, loan files, and account histories are subject to privacy law, contractual limits, and internal controls that rightly restrict who can use them and where. At the same time, some of the scenarios institutions most need to test for, such as fraud patterns, defaults in a downturn, or unusual document formats, are rare in historical data.
Synthetic data in financial services addresses both problems. It is generated to behave like real data statistically, with realistic distributions, correlations, and edge cases, but it does not correspond to actual customers. That allows teams to build and test systems in development environments, share data with vendors for evaluation, and create scenarios that have not yet happened at scale.
Synthetic data is a complement to real data, not a replacement. Models that inform real decisions still need to be validated on representative real-world data before and after deployment.
Synthetic data is most valuable for testing, development, and stress scenarios. Treat it as a controlled input that needs its own validation, not as a shortcut around model risk management.
How Synthetic Data Is Generated
- Rule-based generation: data is created from defined rules and distributions, useful for test cases and system integration testing.
- Statistical modelling: models learn the joint distributions of real data and sample new records that preserve key relationships.
- Generative AI models: deep learning models, including generative adversarial networks and large language models, produce realistic tabular data, text, or documents.
- Simulation: agent-based or economic simulations generate data for scenarios such as rate shocks or regional downturns.
- Privacy techniques: methods such as differential privacy can be applied to limit the risk that any real individual can be re-identified.
Synthetic Data vs Anonymised Data vs Real Data
| Dimension | Real data | Anonymised or masked data | Synthetic data |
|---|---|---|---|
| Contains real customer records | Yes | Yes, with identifiers removed or masked | No |
| Re-identification risk | Not applicable | Can remain through linkage | Lower, but must be tested |
| Covers rare scenarios | Only as observed | Only as observed | Can be generated deliberately |
| Fidelity to reality | Full | High | Depends on generation method and validation |
| Typical use | Production models and decisions | Analytics and some development | Development, testing, sharing, augmentation |
Use Cases for Synthetic Data in Financial Services
- Software and AI testing: test document processing, spreading, and decisioning systems end to end without using live customer files.
- Fraud and AML model development: augment rare fraud and suspicious-activity patterns to improve detection.
- Stress testing and scenario analysis: generate portfolios and borrower behaviour under hypothetical economic conditions.
- Vendor evaluation and data sharing: let third parties evaluate or build on realistic data without receiving customer information.
- Bias testing: construct controlled datasets to probe how models behave across different groups and edge cases.
Risks and Governance
Synthetic data introduces its own risks. Poorly generated data can miss important correlations, exaggerate or erase bias present in the source data, or still leak information about real individuals if the generator memorises records. Institutions should document how synthetic data was produced and from what source, measure its statistical fidelity and privacy protection, restrict its use to approved purposes, and validate any model trained on it against real-world data. When synthetic data is used in developing or testing models that inform decisions, it falls within model risk management, and the generator itself may warrant review.
Frequently Asked Questions
What is synthetic data in financial services?
Why do banks and lenders use synthetic data?
Is synthetic data the same as anonymised data?
Can AI models be trained only on synthetic data?
What are the risks of synthetic data?
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