Synthetic Data Platforms: Synthehol.ai vs Hazy Validation Comparison

Synthetic data platforms are increasingly judged not just on whether they generate realistic data, but on whether they provide quantifiable, auditable proof that the data is fit for purpose. For banks, healthcare providers, and insurers operating under SR 11-7, HIPAA, GDPR, and similar frameworks, trust me, it looks good is not enough. You need validation artifacts—statistical evidence that model risk, compliance, and audit teams can review and defend.

This comparison examines Synthehol.ai Synthetic Data Platform and Hazy through the lens of statistical fidelity and validation reporting, two areas where enterprises evaluating Hazy alternatives are placing increasing scrutiny. Hazy, now part of the SAS portfolio following its November 2024 acquisition, has a strong presence in financial services and regulated industries. Synthehol.ai Synthetic Data Platform is built specifically as a compliance-first synthetic data platform for banking, insurance, and healthcare, with validation artifacts and evidence generation as core product features—not afterthoughts.

High-Level Comparison: Validation Philosophy

At a glance: if your question is Can I show auditors quantitative evidence that this synthetic data preserves the statistical properties I care about, Synthehol.ai Synthetic Data Platform’s validation-first architecture provides that by default. Hazy offers strong privacy and quality positioning, but the depth and granularity of statistical validation artifacts may require additional configuration or custom work.

Statistical Fidelity: What It Means and Why It Matters

Statistical fidelity refers to how closely synthetic data preserves the distributional properties and relationships of the original data. In regulated industries, this is the foundation of whether synthetic data can support:

• SR 11-7 model validation including conceptual soundness, ongoing monitoring, and outcomes analysis
• Fraud and credit risk modeling where subtle correlations matter
• Stress testing and scenario analysis where conditional dependencies drive results
• Vendor data sharing where you need evidence that synthetic data reflects real behavior

Fidelity is typically measured across three layers:

• Univariate fidelity: Do individual columns match their original distributions
• Bivariate or multivariate fidelity: Are correlations and dependencies preserved
• Conditional fidelity: Do conditional distributions such as default rate given score decile remain accurate

Both Synthehol.ai Synthetic Data Platform and Hazy target high fidelity. The difference lies in how explicitly and consistently that fidelity is measured, documented, and delivered to end users.

Synthehol.ai’s Validation-First Architecture

Synthehol.ai Synthetic Data Platform is engineered so that every synthetic dataset comes with a validation pack you can hand to model risk, compliance, or auditors. This is the product’s default behavior.

What’s in a Synthehol.ai validation pack?

For each generation run, Synthehol.ai Synthetic Data Platform automatically produces:

1. Univariate distribution checks

• Kolmogorov-Smirnov tests for all numeric features comparing real vs synthetic distributions
• Distribution plots and empirical CDF overlays for visual inspection
• Summary statistics including mean, median, standard deviation, and percentiles shown side-by-side

2. Multivariate structure validation

• Correlation matrices for real and synthetic data using Pearson and Spearman measures
• Matrix difference heatmaps showing which relationships shifted
• Mean absolute correlation error as a summary metric

3. Conditional and dependency checks

• Key conditional distributions such as utilization vs delinquency or transaction amount by merchant type
• Dependency measures for critical feature interactions
• Segment-level comparisons such as retail vs SME or prime vs subprime

4. Privacy and similarity metrics

• Nearest-neighbor distance distributions to detect memorization risk
• Similarity scores indicating how close synthetic records are to real ones
• Privacy risk summaries in plain language for compliance teams

5. Composite scores

• Fidelity score (0–100) measuring distribution matching and dependency preservation
• Utility score (0–100) measuring model performance trained on synthetic vs real data
• Privacy score (0–100) measuring similarity risk and memorization

These scores answer the key question regulators and auditors ask: Is this synthetic data good enough for the purpose it is being used for?

The 90–95 Percent Fidelity Target

Synthehol.ai Synthetic Data Platform explicitly targets 90–95 percent statistical fidelity across distributions, correlations, and conditional relationships.

Examples include:

• KS statistics typically below 0.05–0.10 for most features
• Correlation preservation typically above 90 percent
• Conditional distributions matching within 5–10 percent across key segments

This level of fidelity makes synthetic data viable for SR 11-7 conceptual soundness and ongoing monitoring.

Hazy’s Quality and Privacy Focus

Hazy built its reputation on high-quality synthetic data for financial services with strong privacy protections. After its acquisition by SAS, Hazy now operates within the broader SAS AI and analytics ecosystem.

Hazy’s quality positioning includes

• Statistically representative synthetic data for analytics and machine learning
• Privacy-first design aligned with GDPR compliance
• Enterprise platform built for financial services with banking and insurance case studies

Hazy provides quality metrics and validation reports, but the depth and automation of those reports can vary depending on configuration.

Validation artifacts in Hazy

Based on available information:

• Quality metrics and reporting are part of the platform
• Privacy risk assessments are core features
• Statistical validation tests such as KS and correlation checks may require configuration or custom implementation

For organizations evaluating Hazy alternatives, the key question becomes whether validation artifacts are automated and standardized enough for model risk and audit requirements.

Validation Reporting: What Model Risk Teams Need

Validation reporting for synthetic data typically needs to satisfy three audiences.

1. Technical users

• Detailed statistical tests and visualizations
• Ability to drill into features and relationships
• Reproducible validation code and configurations

2. Model risk teams

• Summary metrics for SR 11-7 documentation
• Pass or fail thresholds
• Evidence that can be attached to model review packages

3. Compliance and regulators

• Plain-language explanations of data quality and privacy
• Traceability of how the dataset was created and validated
• Defensible methodology understandable without deep ML expertise

Synthehol.ai’s Approach

Synthehol.ai Synthetic Data Platform treats validation reporting as a core product output.

• Every generation job produces a standardized validation report
• Reports are versioned and stored alongside datasets
• Dashboards track validation metrics across multiple runs
• Plain-language summaries translate technical metrics for non-technical stakeholders

This makes validation repeatable and auditable without building custom pipelines.

Hazy’s Approach

With Hazy now integrated into SAS, validation and quality workflows increasingly align with the SAS analytics ecosystem.

This is useful for organizations already operating within SAS environments. However, teams that need standalone validation artifacts may find Synthehol.ai Synthetic Data Platform’s built-in validation packs easier to operationalize.

When to Choose Synthehol.ai vs Hazy

Choose Synthehol.ai Synthetic Data Platform if

• You need automatic per-run validation packs with statistical tests and similarity metrics
• Your compliance team requires quantitative evidence of 90–95 percent fidelity
• You operate in air-gapped or on-premise environments
• You want zero external API or LLM dependencies
• You require fast generation with validation artifacts attached

Choose Hazy if

• You are already using SAS analytics infrastructure
• Privacy and GDPR compliance are your primary concerns
• You prefer a cloud-first managed service approach
• You value established financial services brand presence

The Validation Artifact Gap

For enterprise buyers researching Hazy alternatives or synthetic data validation, the distinction often becomes clear.

• Hazy offers strong privacy and quality positioning with integration into the SAS ecosystem.
• Synthehol.ai Synthetic Data Platform provides a validation-first architecture with automatic statistical evidence generation designed for regulated industries.

For model risk teams asking how to prove synthetic data accuracy to regulators, Synthehol.ai’s approach is straightforward: every dataset ships with validation evidence by default.

Conclusion: Validation Is Not an Afterthought

The synthetic data market is evolving beyond the question of whether realistic data can be generated. The new question is whether that data can be proven fit for purpose.

Statistical fidelity and validation artifacts are now essential requirements for organizations operating under SR 11-7, HIPAA, GDPR, and similar frameworks.

Hazy built a strong reputation around privacy and quality, and its integration into SAS strengthens its position within that ecosystem. However, for enterprises where validation evidence is mandatory, Synthehol.ai Synthetic Data Platform’s validation-first architecture provides automated statistical proof that model risk teams and auditors expect.

If your evaluation criteria for a Hazy alternative include questions such as whether validation artifacts are automatically produced or whether statistical fidelity can be demonstrated with evidence rather than claims, Synthehol.ai Synthetic Data Platform is designed to answer yes by default.