Why are synthetic data and digital twins emerging?

Synthetic data and digital twins are emerging as structural responses to three converging pressures: regulatory data constraints, AI model scalability requirements, and the need for real-time simulation in complex financial ecosystems. Financial institutions operate under strict privacy, conduct and model risk regulation, limiting the free use of production data for experimentation. Synthetic datasets and system-level digital replicas allow controlled innovation without breaching confidentiality or destabilising live environments.

1. Regulatory and Privacy Drivers

Financial services operate under GDPR, banking secrecy rules, and model governance obligations. Using production customer data for model training or stress testing can introduce legal and conduct risk.

Synthetic data enables AI development without exposing identifiable client records, aligning with supervisory expectations on responsible AI.

2. AI and Advanced Analytics Requirements

Modern AI systems (fraud detection, underwriting, churn prediction, portfolio optimisation) require large, diverse and well-labelled datasets. However, real financial datasets often:

• Are imbalanced (e.g., rare fraud events)
• Reflect historical bias
• Lack sufficient edge-case scenarios
• Are siloed across products

Synthetic data allows institutions to:

• Generate rare-event scenarios (e.g., extreme credit stress)
• Balance classes for fraud detection
• Simulate behavioural patterns
• Stress-test model robustness

Digital twins extend this by replicating entire systems — such as liquidity flows, customer journeys, or claims ecosystems — for experimentation.

3. Digital Twins in Systemic Simulation

A digital twin in finance is a dynamic virtual representation of a process, portfolio or ecosystem. Applications include:

Digital twins allow regulators and boards to assess resilience scenarios before deployment, supporting operational risk management.

4. Embedded Finance and Ecosystem Complexity

As financial services become API-driven and ecosystem-integrated, traditional static modelling becomes insufficient. Synthetic data supports:

• Testing multi-party API interactions
• Simulating cross-border compliance flows
• Validating algorithmic fairness in embedded underwriting
• Stress-testing sponsor-bank exposure in platform models

In embedded ecosystems, real-time behavioural modelling is critical to pricing and risk assessment.

5. Competitive and Strategic Implications

Institutions capable of generating high-fidelity synthetic data and maintaining operational digital twins achieve:

• Faster AI deployment
• Lower compliance friction
• Reduced dependency on historical bias
• Enhanced regulatory transparency
• Superior stress-testing capabilities

Those lacking such capabilities risk slower AI innovation and higher model risk exposure.

Strategic Outlook

Synthetic data and digital twins represent a shift from reactive reporting to predictive simulation. In capital-intensive, risk-regulated industries, simulation becomes a competitive differentiator — not merely a technical enhancement.