In today's hypercompetitive financial marketplace, the gap between industry leaders and followers increasingly comes down to one factor: the ability to rapidly innovate using high-quality data. Forward-thinking banks, investment firms, and fintech startups are gaining significant market advantages by experimenting, testing, and deploying new solutions faster than their competitors. Yet this acceleration faces a persistent obstacle – the sensitive nature of financial data severely restricts its availability for development and testing.
This is where synthetic finance data is a major advantage. By generating artificial financial data that perfectly mirrors the statistical properties of real customer information without containing any actual sensitive details, your financial institution can break free from the constraints that have traditionally slowed development cycles.
Top 4 applications of synthetic finance data
- Software development and testing
- Regulatory compliance
- Fraud detection & prevention
- Digital transformation
What is synthetic financial data?
Synthetic financial data is artificially generated information that accurately mimics the statistical patterns, relationships, and properties of real financial datasets without containing any actual customer information. Unlike dummy data or simple randomized values, high-quality synthetic financial data preserves complex correlations between variables — such as the relationship between account balances, transaction histories, credit utilization, and payment behaviors.
Modern approaches use advanced machine learning algorithms to analyze real financial data, identify its underlying statistical distributions and relationships, and then generate entirely new records that maintain these properties. The result is a dataset that looks and behaves like real financial data for analytical purposes but cannot be traced back to any real individual or account.
High-quality de-identified financial data is particularly valuable due to the regulatory nature of the industry. Properly generated synthetic data has broad applications for companies looking to improve cybersecurity, customer experience, compliance, business growth and more.
How financial organizations use synthetic data
Let's examine how specific sectors within finance are leveraging synthetic finance data to solve their most pressing challenges.
Software development and testing
For engineers building financial applications, the testing environment has traditionally been a significant constraint. Production-quality data is essential for validating complex financial logic, but compliance requirements often restrict access to realistic datasets.
Synthetic data generators allow you to configure specific parameters — like transaction types, card distributions, anomaly rates, and volume patterns — to test against millions of transaction scenarios, including edge cases that might occur only once in thousands of real transactions. You can even integrate synthetic data workflows directly into your CI/CD pipelines so you can run automated testing against fresh synthetic datasets with each build.
Regulatory compliance
Companies often allocate substantial resources to compliance-related development due to the complex regulatory environment. The financial sector faces some of the strictest regulations globally, with frameworks like PSD2, SOX, MiFID II, and Basel III all requiring specific technical implementations and controls.
With synthetic finance data, your teams can develop and test financial applications while mitigating the potential exposure of real customer information, as required by data privacy regulations like GDPR, CCPA, and PCI. By replacing raw production data with high-fidelity synthetic versions, you can build and test more confidently, using workflows that are better aligned with modern privacy mandates — which reduces your risk of the legal and financial risks of noncompliance.
Fraud detection & prevention
According to a 2025 paper by the Federal Reserve, the scarcity of publicly available payment transaction data — combined with the low prevalence of fraud in overall volume — makes it difficult to develop and evaluate effective fraud detection models using real world data alone. Synthetic data generation allows you to:
- Analyze patterns from known fraud cases
- Generate synthetic fraud scenarios
- Balance training datasets
- Validate model performance against varied attack vectors
Financial institutions can then generate libraries of synthetic transactions that mimic sophisticated fraud techniques — from card testing to account takeovers — and build more resilient detection systems that identify emerging attack patterns before they impact customers.
Creating high quality synthetic financial data
The technical challenges of generating realistic financial data have led to several specialized approaches that balance fidelity with privacy. Understanding the trade-offs of each approach will help you identify the right solution for your specific use case.
3 ways to create high quality synthetic financial data
1. Model-based (or statistical) data synthesis
Model-based synthesis uses advanced machine learning techniques to capture the statistical distributions and complex correlations within financial datasets. For example, Tonic.ai's statistical modeling capabilities analyze how variables relate to each other in real financial data, then generate entirely new records that maintain these relationships.
This approach is particularly effective for complex financial datasets like trading data where volume, volatility, and price movements are intrinsically linked.
2. Rules-based data synthesis
For datasets with well-defined business rules and constraints, Tonic.ai's rules-based synthesis allows teams to explicitly encode domain knowledge into the generation process. You can define constraint systems that ensure your synthetic data maintains internal consistency so that account balances match transaction histories, credit limits align with income levels, or loan-to-value ratios stay within regulatory bounds.
Tonic.ai's platforms make it easy to configure these rules, providing precise control over the output and enabling on-demand generation of edge cases for thorough testing.
3. Data de-identification with referential integrity
Modern financial applications depend on complex relational databases where referential integrity is critical. Tonic.ai’s advanced de-identification platform, Tonic Structural, transforms production data into synthetic versions that preserve relationships between entities while removing all personally identifiable information. This approach maintains foreign key relationships, transaction chains, and other critical structures that simplistic approaches to anonymization would break, enabling realistic testing of joins, aggregations, and multi-table operations without privacy risks — a capability that's particularly valuable for core banking systems with complex data relationships.
Potential challenges of using synthetic data in finance
While synthetic finance data offers significant advantages, there are some challenges to consider. Here's how Tonic.ai helps address these common hurdles:
- Accuracy and realism in replicating complexity: Financial data contains intricate patterns and correlations that can be difficult to preserve—Tonic.ai's advanced modeling capabilities ensure that generated data maintains statistical fidelity across multiple dimensions.
- Correlation and dependency maintenance: Preserving relationships between financial variables is challenging—Tonic.ai's platform specifically preserves these critical interdependencies through sophisticated relationship modeling that maintains referential integrity throughout your data model.
- Regulatory considerations: Financial institutions must ensure synthetic data generation techniques satisfy regulatory requirements — Tonic.ai incorporates differential privacy mechanisms and provides detailed documentation to support compliance verification and audit requirements.
Advancing financial innovation through quality data
As financial institutions continue their digital transformation journey, synthetic data provides a strong foundation for innovation without the constraints of working with sensitive customer information. Leading organizations are already incorporating synthetic data generation into their core development practices, enabling faster innovation cycles while maintaining strict compliance with privacy regulations.
Ready to accelerate your financial software development with synthetic data? Book a demo with Tonic.ai today.
A bilingual wordsmith dedicated to the art of engineering with words, Chiara has over a decade of experience supporting corporate communications at multi-national companies. She once translated for the Pope; it has more overlap with translating for developers than you might think.
