Investment firms must accelerate innovation through data-driven insights while safeguarding sensitive client information under stringent regulatory frameworks. Traditional approaches, such as data masking, anonymization, or restricting access, often diminish data utility or slow development. This is where synthetic data comes in.
Synthetic data refers to artificially generated information that replicates the statistical properties of real datasets without exposing actual client records. This technology is transforming how firms conduct security testing, strengthen fraud detection, and demonstrate regulatory compliance.
Understanding Synthetic Data: Beyond Traditional Anonymization
Synthetic data represents a departure from conventional data protection. Rather than obscuring sensitive fields in production data, this type of data generation creates entirely new datasets that maintain the statistical characteristics of real information while containing no actual personally identifiable information. As MIT-Watson AI Lab researchers working with Wells Fargo discovered, synthetic data achieves three goals simultaneously: scalability, efficiency, and privacy.
How Synthetic Data Works
Advanced machine learning techniques power this transformation:
- Generative Adversarial Networks (GANs): Two neural networks—a generator and a discriminator—work in tandem to produce realistic synthetic records. The generator creates artificial data while the discriminator evaluates authenticity. It iteratively refines outputs until synthetic data becomes statistically indistinguishable from real data.
- Variational Autoencoders (VAEs): These encode real data into a latent space before decoding with modifications to generate novel yet realistic records.
- Multi-Modal Generation: These AI models create flexible, high-fidelity data across time-series market data, tabular transaction records, and textual sentiment information.
According to Gartner research, synthetic data offers investment firms opportunities to advance financial crime prevention, accelerate innovation, and enhance risk management. They can do this while maintaining privacy protections that traditional methods struggle to deliver.
Revolutionizing Security Testing Without Exposing Client Data
Traditional security validation faces a constraint: testing requires realistic data, yet production data contains sensitive client information that regulations prohibit from broad exposure. This creates what Wells Fargo executives describe as an “unsustainable” bottleneck for product development.
Three Security Testing Capabilities Unlocked
Synthetic data changes this equation by enabling:
- Negative testing for rare, high-impact attack vectors, including scenarios not present in historical records.
- Safe collaboration with third-party security vendors for red-teaming and penetration testing, eliminating concerns about data breaches during security assessments.
- Multi-table development environments with realistic, referentially consistent data, enabling comprehensive integration testing across complex systems.
IBM’s synthetic data tool for instant-payment fraud detection exemplifies this shift. As Bloomberg describes, the platform creates lifelike datasets mimicking real-world conditions for platforms like Venmo, PayPal, and Zelle, allowing financial institutions to train AI models that detect and flag fraudulent transactions in real time. If malicious actors attempt to extract training data, they encounter only synthetic records containing no personally identifiable information.
The MIT-Watson AI Lab’s example is particularly sophisticated: their synthetic database generation creates multiple tables that referentially relate across entire database structures through hierarchical modeling. This enabled Wells Fargo to provision complete development environments with realistic, non-sensitive data, dramatically accelerating security validation cycles while maintaining privacy protections.
Enhancing Fraud and Financial Crime Detection Systems
Fraud detection presents a challenge for investment firms. Fraudulent transactions are rare by design, creating dataset imbalances that hinder model training.
Traditional fraud detection models struggle with the low prevalence of fraud in the overall volume of transactions that must be processed. This makes it difficult to develop effective detection systems using real-world data alone. Privacy mandates compound this scarcity, as sensitive data must be securely locked away.
Addressing Fraud Detection Challenges
Synthetic data addresses both constraints by:
- Generating diverse transaction scenarios, including rare events not captured in historical records, enabling firms to train robust models against the full spectrum of potential fraud patterns.
- Simulating entirely new fraud tactics that haven’t yet materialized, creating what Finextra describes as the ability to “model unprecedented market conditions” and prepare detection systems for “black swan” events.
- Reducing false positives: In an example shared by Bloomberg, IBM’s synthetic data generator produced large-scale lifelike datasets, enabling them to “train and test AI models safely and efficiently, without ever using actual customer data.” The resulting models significantly reducing false positives in fraud detection.
Anti-Money Laundering Applications
AI systems trained on biased or incomplete datasets can inadvertently inherit those biases, consistently flagging transactions based on patterns reflecting prejudice rather than genuine financial crime. Synthetic data offers a solution: by employing data that simulates realistic scenarios without real-world limitations, AI can learn to identify genuine financial crimes more effectively while mitigating bias. According to FinTech Global analysis, this “compliance-first” approach aligns with specific risk appetites and regulatory requirements while preserving human oversight.
The market opportunity is substantial. The synthetic data generation market is projected to grow from $313.5 million in 2024 to $6.6 billion over the next decade, driven largely by financial services applications, Bloomberg reports. As the CFA Institute observed in its 2025 report, this technology stands “at the frontier of innovation in investment management… offering not just a workaround to data scarcity, but a catalyst for smarter, faster, and more resilient decision-making.”
Meeting Regulatory Expectations for Compliance Demonstrations
Now, regulators worldwide are acknowledging synthetic data’s potential role in compliance frameworks. For example, the UK Financial Conduct Authority established the Synthetic Data Expert Group in February 2023 to explore its use in financial markets. Their August 2025 report emphasizes that synthetic data “offers a powerful way to unlock the value of data, enable experimentation, model development, and broader innovation across the financial system.”
Regulatory Acceptance and Requirements
Key developments include:
- Compliance testing acceptance: Regulators increasingly accept synthetic data for compliance testing and model validation. Forbes reports that synthetic data aids regulatory compliance by “generating data scenarios that help validate the effectiveness of compliance models,” as Forbes reports.
- Privacy regulation compliance: Because properly generated synthetic data contains no real customer information, it sidesteps the strictest privacy requirements under GDPR and CCPA while preserving analytical utility.
- Governance requirements: The World Economic Forum warns that synthetic data must meet rigorous standards to ensure it doesn’t inadvertently leak sensitive traits or amplify underlying biases. Business leaders must prioritize oversight and compliance, building robust traceability and provenance systems.
The FCA’s governance framework confirms that responsible adoption of synthetic data is fundamentally a Model Risk Management issue requiring integration of synthetic-data-specific controls into existing frameworks. Organizations must maintain comprehensive audit trails demonstrating ongoing compliance with privacy preservation measures. Key principles include validating models, managing their limitations, ensuring clear accountability, and maintaining transparency throughout the synthetic data lifecycle.
Best Practices for Implementation: Building and Governing Synthetic Datasets
Successfully implementing synthetic data requires a structured approach that balances technical sophistication with rigorous governance.
Implementation Roadmap
Creating high-quality synthetic datasets involves several coordinated steps.
Firms should start by identifying the specific problem synthetic data will address—such as fraud detection, stress testing, or software testing environments. Next, they should gather representative real-world data, train generative models (like GANs or VAEs) to learn their statistical patterns, and generate synthetic datasets that mirror these characteristics.
Validation is essential. Teams should use quantitative methods to confirm realism and reliability, such as statistical testing and “train-on-synthetic, test-on-real” comparisons. Regular updates help prevent model drift and keep data aligned with real-world conditions.
Collaboration between technical teams and business stakeholders ensures that synthetic data reflects operational logic, producing datasets that are both compliant and useful across analytics, compliance, and development use cases.
Governance Framework Essentials
Organizations must establish:
- Traceability systems that document how and when synthetic data was introduced, enabling accountability and reducing risks like bias amplification
- Comprehensive documentation and version control of generation processes, including methods used, assumptions made, and decisions taken
- Policy-as-code approaches using declarative formats that promote consistency, enable peer review of privacy rules, and provide transparent audit trails
- Privacy by design: Techniques including differential privacy, data masking, and anonymization of training inputs minimize leakage risk
Collaboration and Validation
IBM emphasizes that working with subject matter experts who understand the nuances of financial data helps generate synthetic datasets that accurately reflect real-world scenarios, patterns, and edge cases. Models should be audited for memorization using membership inference tests or canary insertion methods—validation that’s especially critical in sectors governed by strict compliance frameworks.
Challenges persist despite best practices. Financial institutions cite talent shortages, integration difficulties with legacy infrastructure, and cost considerations as ongoing barriers. A survey from CFO Dive found data challenges now top the list of headwinds for financial services companies pursuing AI benefits, with 60% of respondents planning to increase investments in computing infrastructure and optimizing AI workflows.
Strategic Roadmap: Positioning Synthetic Data at the Core of Financial Data Protection
Investment firms should approach synthetic data adoption with measured experimentation, recognizing transformative potential while acknowledging the absence of fully standardized frameworks. The CFA Institute recommends starting with simpler, more transparent methodologies, then progressing to sophisticated models while frequently evaluating performance against real-world data.
The Strategic Value Proposition
Synthetic data enables firms to:
- Unlock data’s full potential while adhering to privacy principles
- Move beyond traditional anonymization limitations to offer secure collaboration, accelerated innovation, and robust AI development
- Realize significant value through strategic data sharing
Three Near-Term Priorities
- Secure stakeholder buy-in by articulating the business case: faster innovation cycles, reduced compliance risk, enhanced security testing, and competitive advantage through superior fraud detection.
- Launch demonstrator projects in controlled environments. Start with development and testing use cases before expanding to production fraud detection or compliance validation.
- Establish robust governance frameworks that provide the transparency, traceability, and accountability that regulators increasingly demand.
The regulatory landscape continues evolving in synthetic data’s favor. As the FCA’s Synthetic Data Expert Group concluded, this technology offers powerful capabilities to create value, support experimentation, and drive innovation across the financial system. Early adopters who invest in developing capabilities to generate and utilize high-fidelity, privacy-preserving synthetic information will gain significant competitive advantages.
Balancing Between Innovation and Privacy with Synthetic Data
Synthetic data is rapidly becoming a core driver of advanced, compliant financial data protection. It reframes the relationship between innovation and privacy. Investment firms no longer face a tradeoff between leveraging data for competitive advantage and safeguarding client information under rigorous regulations. Properly implemented synthetic data offers a path to achieving both objectives.
Financial Services Security with Option One Technologies
Option One Technologies partners with investment firms to strengthen security, accelerate innovation, and ensure compliance. Contact us today to explore how synthetic data solutions can transform your financial data protection approach while unlocking your data’s full strategic potential.
