Can Synthetic Data Accelerate AI Innovation While Strengthening Model Governance in Financial Services?

Financial institutions rely on large volumes of data to power fraud detection, credit risk assessment, market surveillance, and financial crime monitoring. Transaction histories, account activity, and payment networks generate valuable signals for machine learning models. At the same time, this information contains highly sensitive personal and financial details that are subject to strict privacy regulations and internal governance controls.

These constraints often limit how organisations access and share production datasets for analytics development. Synthetic data has emerged as a practical capability that helps institutions address this challenge. Machine learning models can generate artificial datasets that reproduce statistical patterns present in real financial data without replicating identifiable customer records.

Industry analysts expect the role of synthetic data to expand significantly. Gartner research projects that synthetic data will account for the majority of data used for AI and analytics projects by 2030. As financial institutions expand AI adoption, synthetic data increasingly supports experimentation, model development, and collaboration across financial ecosystems.

Expanding the Data Foundation for Financial AI

AI models in financial services require extensive, diverse datasets to identify complex behavioural patterns. Fraud detection systems analyse large volumes of payment transactions. Credit risk models evaluate a borrower's history and repayment patterns. Anti-money laundering systems monitor transaction networks across multiple institutions.

Real datasets frequently present structural limitations. Fraud transactions represent a very small percentage of total activity. Rare risk scenarios occur infrequently. These constraints limit machine learning models' ability to learn from diverse patterns.

Synthetic data generation enables institutions to generate additional data points that replicate the statistical properties of rare scenarios while preserving the structure of the original dataset. This approach enables data scientists to simulate edge cases and uncommon transaction patterns during model development.

Research from the Bank for International Settlements Innovation Hub demonstrates how synthetic datasets can support financial analytics while protecting confidentiality. In Project Hertha, conducted with the Bank of England, researchers developed a synthetic transaction dataset containing 1.8 million simulated bank accounts and 308 million transactions to study AI-driven financial crime detection without using real customer data. The experiments showed that system-level analytics could identify 12% more illicit accounts and improve the detection of previously unseen financial crime patterns by 26%.

Regulatory Initiatives Supporting Synthetic Data

Regulators have also begun exploring synthetic datasets to support innovation in financial services. These initiatives aim to create controlled environments where developers can experiment with analytics tools without accessing real customer data.

The Financial Conduct Authority (FCA) in the United Kingdom launched a Digital Sandbox program that provides synthetic financial datasets for fintech developers and financial institutions. The sandbox allows participants to test models and technology solutions using representative financial data while maintaining strict privacy safeguards.

One major project within the sandbox focused on Authorised Push Payment fraud, a rapidly growing category of payment scams. The FCA released a synthetic dataset designed to simulate financial activity across approximately 20,000 individuals and businesses over two years, including payment flows and account activity linked to fraud events. Developers can use the dataset to train and evaluate fraud detection algorithms without accessing real banking records.

This initiative demonstrates how regulators can enable innovation while maintaining strong governance controls around financial information.

Real-World Use Cases in Financial Crime and Fraud Analytics

Synthetic datasets have also supported research and experimentation in anti-money laundering analytics. A collaboration between Aarhus University and Spar Nord Bank produced one of the first large-scale synthetic datasets designed for financial crime detection.

Researchers generated a dataset containing 16 million synthetic financial transactions and approximately 20,000 alerts representing suspicious activity patterns used in anti-money laundering monitoring systems. The dataset enabled researchers to evaluate machine learning models for detecting illicit financial behaviour without relying on real banking records.

The project demonstrated that synthetic datasets can replicate the statistical characteristics required to train financial crime detection systems while protecting confidential customer information.

Large financial institutions have also used synthetic transaction data to enhance fraud-detection analytics. Organisations such as American Express and JPMorgan Chase have explored synthetic datasets to simulate transaction patterns and improve machine learning models that identify fraudulent activity.

Synthetic data allows analytics teams to replicate rare fraud scenarios that appear infrequently in historical transaction records. These simulations expand the range of behavioural patterns available for model training and testing.

Model Governance and Data Quality Considerations

Synthetic data introduces additional considerations for model governance frameworks. Artificial datasets are derived from real data distributions and may reproduce the statistical patterns present in the source data.

Financial institutions, therefore, integrate synthetic data generation into their existing governance and validation processes. Model validation teams evaluate whether synthetic datasets accurately reflect real financial behaviour and whether models trained on them perform reliably in production environments.

The FCA has highlighted the importance of integrating synthetic data practices into governance frameworks for model development and validation. Institutions must assess statistical fidelity, monitor model performance, and document the methods used to generate artificial datasets.

Validation processes often include comparisons between synthetic and original data distributions, stress testing of machine learning models, and performance evaluation using real datasets.

These practices ensure that synthetic data strengthens analytics development while maintaining the reliability and accountability required in financial systems.

A Strategic Data Capability for Financial Innovation

Artificial intelligence continues to reshape financial services. Institutions deploy machine learning models to detect fraud, identify financial crime, assess credit risk, and optimise customer experiences. These capabilities depend on access to high-quality datasets that support reliable model training.

Synthetic data expands the available data foundation while maintaining strong privacy safeguards and governance controls. Regulators, research institutions, and financial organisations already use artificial datasets to enable experimentation and collaborative innovation.

The long-term impact of synthetic data will depend on how effectively institutions integrate it into their analytics infrastructure and model governance frameworks. Organisations that combine synthetic data generation with rigorous validation and transparent oversight can accelerate AI development while maintaining trust in their systems.

In a sector where innovation and accountability must advance together, synthetic data provides a practical approach to unlocking the value of financial data while preserving the integrity of the financial ecosystem.