The top synthetic data vendors for banks and financial services in 2026 include MOSTLY AI, SAS Data Maker (formerly Hazy), Tonic.ai, Syntho, K2view, Syntheticus, and DataCebo, among others. The right choice depends on your institution’s specific use cases, regulatory environment, and existing data infrastructure. This article walks through how these vendors work, what to look for, how they handle compliance, and what the investment looks like.

How do synthetic data vendors actually serve financial institutions?

Synthetic data vendors serve financial institutions by generating statistically accurate, privacy-safe datasets that replace or supplement real customer and transaction data. They enable banks to train AI models, test systems, share data across departments, and meet regulatory requirements without exposing sensitive personal information. Most enterprise vendors offer direct integration with existing data lakes and analytics pipelines, making deployment a practical reality rather than a research project.

At a technical level, vendors use a combination of statistical modeling, generative AI techniques such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), and hybrid approaches that blend real and synthetic data. The choice of method affects the realism of the output and the degree of privacy protection provided, which is why leading vendors offer built-in quality reporting rather than leaving validation entirely to the customer.

One of the most compelling practical benefits is secure data sharing. A bank’s risk, marketing, and product development teams often cannot access the same datasets due to internal privacy controls. Synthetic data lets those teams work from functionally equivalent datasets without any of the compliance friction. The same logic applies to sharing data with external fintech partners for joint analytics or product development.

ING Belgium demonstrated this kind of operational value clearly when it used machine-learning-generated payment data to validate SEPA systems, achieving dramatically broader test coverage in a fraction of the usual time while staying within strict data-handling rules. That kind of result is increasingly the expectation, not the exception, as synthetic data becomes foundational to how banks test and build.

What should banks look for when evaluating synthetic data vendors?

Banks evaluating synthetic data vendors should assess three core dimensions: utility (how well the synthetic data performs on the intended task), fidelity (how closely it replicates the statistical properties of the original), and privacy (resistance to re-identification attacks). Beyond these technical measures, non-negotiable requirements include regulatory compliance features, encryption standards, access controls, and defensible audit trails.

The UK Financial Conduct Authority’s Synthetic Data Expert Group, which published updated governance guidance in August 2025, identified validation as one of the most persistent barriers to adoption. Many institutions select a vendor based on feature lists or price without accounting for whether the vendor’s validation framework actually maps to their specific use case. A fraud detection model and a credit risk model have very different data requirements, and a vendor that performs well on one may not be the right fit for the other.

Governance infrastructure deserves particular attention. Audit trails that work in a standard data pipeline may not translate cleanly to a synthetic data workflow. Banks need vendors who have built lineage tracking and compliance reporting into the product itself, not as an afterthought. SAS Data Maker, for example, was specifically designed with lineage, audit logs, and defensible privacy measures as core features rather than optional add-ons.

There is also a calibration risk that is easy to overlook. A synthetic data generator that is too accurate risks reproducing fragments of the original training data, which creates re-identification exposure. One that is too inaccurate produces datasets that mislead data science teams. The best vendors offer transparent quality metrics that let your team find the right balance for each use case, rather than treating accuracy as a single dial to maximize.

For highly regulated environments, vendors with a proven compliance track record in financial services, such as MOSTLY AI, Syntho, and SAS Data Maker, are generally better starting points than general-purpose tools that happen to support tabular data.

What are the top synthetic data vendors for banking and financial services?

The leading synthetic data vendors for banking and financial services in 2026 are MOSTLY AI, SAS Data Maker, Tonic.ai, Syntho, K2view, Syntheticus, DataCebo, and YData. Each has a distinct focus and deployment model, so the best choice depends on your institution’s size, regulatory context, and technical environment.

MOSTLY AI

MOSTLY AI is a Vienna-based vendor focused exclusively on privacy-safe tabular synthetic data. It is one of the most widely cited platforms in financial services, with known deployments at major European and global institutions. The platform includes built-in quality reports covering fidelity, utility, and privacy metrics, and its subscription-based pricing model makes it accessible to mid-market banks and insurers without requiring large upfront capital commitments.

SAS Data Maker

SAS Data Maker emerged from SAS’s acquisition of Hazy, a London-based synthetic data pioneer, in late 2024. The integrated product launched on the Microsoft Azure Marketplace in late 2025 and is being extended to AWS, Google Cloud, and Snowflake. It is built for regulatory readiness, with lineage tracking and audit logs designed to meet the documentation requirements of financial regulators. One UK financial services firm reported a meaningful improvement in credit scoring model accuracy after adopting the platform.

Tonic.ai

Tonic.ai handles both structured tabular data and unstructured text, which makes it useful for institutions dealing with mixed data environments. It offers SDKs for Python and direct database connectors, and its enterprise edition supports synthetic data scaling across multi-cloud environments. This flexibility makes it a strong fit for banks with complex, distributed IT architectures.

Syntho

Syntho is a privacy-first, GDPR-compliant platform with a strong focus on financial services. It supports fraud detection model development, open banking data sharing, and product development acceleration. The Syntho Engine includes time-series data support and quality assurance reporting, which are particularly relevant for transaction data and behavioral analytics use cases.

K2view

K2view takes a business-entity-driven approach to synthetic data generation, preserving complex relationships across systems. This is especially valuable in banking environments where customer data spans dozens of interconnected tables and systems. K2view was recognized as a Gartner Visionary in the Magic Quadrant for Data Integration Tools for the third consecutive year in 2025, reflecting its strength in enterprise-grade deployments.

Other notable vendors

Syntheticus targets retail banking specifically, using Differential Privacy and Trusted Execution Environments to protect sensitive financial data during generation. DataCebo, makers of the open-source Synthetic Data Vault, offers an enterprise version well-suited to on-premises deployments for fraud testing and ML training. YData’s Fabric platform combines automated data profiling with synthetic generation and supports both no-code and SDK-based workflows. Gretel, formerly an independent synthetic data startup, was acquired by NVIDIA in early 2025, and its technology is now integrated into NVIDIA’s cloud-based generative AI services.

We at bluegen.live are also active in this space, built specifically for the European market with GDPR compliance by design. Our platform supports complex relational data structures, including many-to-many relationships, making it a practical choice for regulated industries like finance, healthcare, and energy where data relationships are rarely simple.

How do synthetic data vendors handle regulatory compliance in finance?

Reputable synthetic data vendors handle regulatory compliance in finance by building privacy-enhancing technologies directly into their generation pipelines, providing audit trails and lineage documentation, and aligning their products with frameworks such as GDPR, CCPA, and sector-specific regulations like the EU’s Digital Operational Resilience Act (DORA). The strongest vendors treat compliance as a product feature, not a consulting add-on.

The regulatory landscape for financial institutions is genuinely complex. Privacy laws now cover the vast majority of the global population, and financial institutions operating across borders face overlapping and sometimes conflicting requirements. DORA, which came into force for EU financial institutions in January 2025, adds standardized cybersecurity and operational risk requirements that directly affect how synthetic data pipelines must be secured and audited.

One important nuance that banks need to understand: generating synthetic data from real personal data triggers compliance obligations under GDPR and similar frameworks, even if the final synthetic dataset contains no personally identifiable information. The initial processing of real data to create synthetic datasets requires a lawful basis, may trigger data subject rights, and should be covered by a privacy impact assessment. This is why the FCA’s governance guidance emphasizes validation and documentation rather than treating synthetic data as automatically exempt from oversight.

Vendors that combine synthetic data generation with privacy-enhancing technologies such as differential privacy, federated learning, or confidential computing provide the strongest compliance posture. Syntheticus, for example, uses both Differential Privacy and Trusted Execution Environments. These technical controls provide a defensible basis for demonstrating that privacy protections are robust, not just assumed.

The EU Digital Finance Platform has gone further by making supervisory synthetic datasets available to institutions developing and validating AI and ML solutions, with formal testing to verify that statistical properties are preserved. This kind of regulatory endorsement signals that synthetic data is moving from a workaround to an accepted part of the compliance toolkit in financial services.

What’s the difference between synthetic data and data anonymization for banks?

The key difference is that anonymization modifies real data to remove or obscure identifiers, while synthetic data is entirely fabricated to replicate the statistical patterns of real data without containing any actual records. For banks, this distinction has practical consequences for privacy risk, analytical utility, and regulatory treatment.

Anonymization is simpler and faster to implement. It preserves closer fidelity to the original data for certain analyses because it starts from real records. However, it consistently degrades data utility, and residual re-identification risk remains, particularly in datasets where combinations of attributes can still point to specific individuals. For AI and machine learning training at scale, anonymized data is often too limited in volume and too constrained in diversity to be genuinely useful.

Synthetic data addresses these limitations directly. Because it is generated from scratch using statistical models trained on real data, it can be produced at any volume, augmented with rare scenarios, and shared freely without the re-identification concerns that follow anonymized data. It is particularly well-suited to training fraud detection models, where the scarcity of genuine fraud examples is a persistent challenge that anonymization alone cannot solve.

The trade-off is implementation complexity. Generating high-quality synthetic data requires careful statistical modeling, calibration, and validation. Done poorly, a synthetic dataset can mislead data science teams just as easily as it can help them. The more nuanced strategic question for regulated teams is not which method is technically superior, but which one creates a better long-term operating model for their specific data environment. For institutions with ambitions to reduce their dependence on production-derived data across non-production environments, synthetic data offers a path that anonymization simply cannot provide.

Which financial use cases benefit most from synthetic data?

The financial use cases that benefit most from synthetic data are fraud detection and anti-money laundering model training, credit risk modeling, stress testing, regulatory sandbox testing, and cross-border data sharing. These are areas where data scarcity, privacy restrictions, or the need for rare-event scenarios make real data either unavailable or insufficient on its own.

Fraud detection and AML

Fraud detection is arguably the highest-value use case. Real fraud datasets are inherently imbalanced because genuine fraud is rare relative to legitimate transactions. Synthetic data allows institutions to generate realistic fraudulent transaction patterns at scale, creating balanced training sets that significantly improve model performance. Banks using augmented synthetic datasets have reported meaningful improvements in fraud detection accuracy, and synthetic transaction data consistently achieves high utility equivalence to production data in testing environments.

Credit risk and stress testing

Credit risk modeling benefits from synthetic data’s ability to simulate customer profiles that do not yet exist in historical records, including thin-file customers or novel credit products. Synthetic data also enables the creation of realistic stress scenarios, including rare economic shocks that have never appeared in a bank’s actual portfolio. Investment banks have used synthetic market data to model black swan events for risk management purposes, scenarios that real historical data simply cannot cover with sufficient frequency.

Cross-border data sharing and open banking

One of the most practically significant use cases is enabling data collaboration across jurisdictions without moving personal data. A North American bank demonstrated this by training AML models across four countries using hybrid synthetic and real data, achieving cross-border compliance without the legal complexity of international data transfers. In open banking contexts, synthetic data allows institutions to share analytically useful datasets with fintech partners without exposing customer records.

Financial institutions typically use only a fraction of their available data for generating insights because so much of it is locked behind access restrictions. Synthetic data helps unlock that potential by removing the privacy barriers that prevent data from flowing to the teams and tools that need it most.

How much does synthetic data generation cost for financial institutions?

The cost of synthetic data generation for financial institutions varies widely based on the complexity of your data environment, the use cases you need to support, and the vendor’s pricing model. Enterprise deployments typically involve a combination of platform licensing, usage-based metering, and professional services, and financial services institutions should expect to pay a premium relative to less regulated industries due to the additional compliance and validation requirements involved.

Pricing across the vendor landscape has converged toward a structure that combines a platform base fee, variable usage metering, and optional professional services. MOSTLY AI publishes a subscription model with tiers suitable for mid-market institutions. Tonic.ai offers entry-level per-user pricing for smaller teams, with enterprise quotes for larger deployments. Vendors like Syntho, SAS Data Maker, K2view, and DataCebo operate on custom enterprise pricing that reflects the specifics of each deployment.

Several factors drive cost upward in financial services specifically. The regulatory complexity of financial data requires more rigorous validation, more detailed audit trails, and often more professional services engagement to configure the pipeline correctly. Time-series data, which is common in transaction and market data contexts, is technically more demanding to synthesize than static tabular data and typically commands higher pricing. Multi-cloud or on-premises deployment requirements also add to the total cost of ownership compared with cloud-native SaaS implementations.

The investment case for synthetic data in banking is strong when framed against the alternatives. The average cost of a data breach provides a substantial baseline for calculating risk-adjusted ROI. Beyond risk mitigation, organizations that adopt synthetic data consistently report significantly faster development cycles because teams no longer wait for privacy approvals or data access provisioning before starting work. McKinsey has estimated that generative AI, supported by synthetic data, could unlock hundreds of billions of dollars in annual value for the banking sector globally, which puts even significant vendor investments in perspective.

If you are evaluating the investment for your institution, the most useful starting point is defining your specific use cases clearly before approaching vendors. The cost of a fraud detection deployment looks very different from a credit risk modeling initiative, and vendors will price accordingly. Requesting a demo from shortlisted vendors with your actual use case in hand will give you a much more accurate picture of what the investment looks like for your organization than any published price range can provide.

How BlueGen helps banks and financial institutions with synthetic data

Choosing the right synthetic data vendor is one challenge — getting that data to work reliably within a regulated financial environment is another. BlueGen was built specifically for this context, combining GDPR-compliant synthetic data generation with the structural depth that financial data demands.

• Complex relational data support: BlueGen handles many-to-many relationships and deeply nested data structures, preserving the integrity of customer, transaction, and product relationships that simpler tools flatten or break.
• Compliance by design: Privacy-enhancing controls are built into the generation pipeline, not bolted on afterward, giving compliance and legal teams a defensible basis for each synthetic dataset produced.
• European regulatory alignment: BlueGen is purpose-built for the European market, with GDPR and DORA considerations embedded into the platform architecture from the ground up.
• Faster development cycles: Teams can access realistic, privacy-safe data immediately, without waiting for access approvals or anonymization workflows that slow down model development and testing.
• Validated output quality: Built-in fidelity, utility, and privacy metrics give data science and risk teams the transparency they need to trust the synthetic datasets they work with.

If you are evaluating synthetic data vendors for a financial services use case and want to see how BlueGen performs against your specific data environment, request a demo and bring your actual use case to the conversation.