Generative AI and Synthetic Data

September 24, 2025 · 10min

In 2025, generative AI is rapidly reshaping how data scientists think about data augmentation, anonymization, and model training—especially in domains with strict privacy constraints. In this blog, we’ll cover:

What synthetic data is, and why it matters
How generative models (GANs, diffusion models, LLMs) are applied to create synthetic data
Practical use-cases in finance, healthcare, analytics
Challenges, pitfalls, and best practices
Outlook: where synthetic data is heading

What Is Synthetic Data, and Why It Matters

Synthetic data refers to data that is artificially generated (by algorithms) to mimic the statistical properties, structure, and relationships of real-world datasets, without directly revealing identifiable information.

Key motivations:

Privacy & compliance: In sectors like healthcare, finance, and insurance, strict regulations (e.g. HIPAA, GDPR) restrict use or sharing of real sensitive records. Synthetic data offers a privacy-respecting alternative. (PMC)
Data scarcity & class imbalance: Some events (fraud, rare disease cases) are infrequent. Synthetic data can artificially upsample rare classes or simulate edge-case scenarios. (humansintheloop.org)
Safe sharing & collaboration: Organizations can share synthetic datasets with partners or external researchers without risking exposure of sensitive data. (AIMultiple)
Faster prototyping / validation: You can generate custom scenarios on demand, test model robustness, validate under stress conditions. (tredence.com)

However, synthetic data is not a silver bullet. The generated data must maintain utility (i.e., support model performance) and fidelity (i.e., follow real distributional patterns) without leaking sensitive information.

How Synthetic Data is Created Using Generative Models

Broadly, synthetic data generation can leverage different classes of generative models. Below are some of the commonly used approaches as of 2025.

GANs (Generative Adversarial Networks)

A generator network produces synthetic samples; a discriminator network tries to distinguish real vs synthetic. Over adversarial training, the generator learns to produce realistic samples.
Variants exist for tabular, image, time-series, and mixed-modal data.
In healthcare, recent work proposes bias-transforming GANs (Bt-GAN) to generate fair synthetic EHR data by constraining spurious correlations and preserving subgroup densities. (arXiv)
In finance, GANs have long been considered for simulating transaction data, though more recently diffusion-based models are gaining attention. (arXiv)

Diffusion Models & Score-Based Generative Models

Diffusion models gradually corrupt data by adding noise, and then learn to reverse the process to sample from the clean distribution.
They are gaining prominence for tabular data generation because they often avoid some of the mode-collapse issues of GANs.
For instance, FinDiff is a diffusion-based model tailored for financial tabular data that generates mixed-type attributes (numeric, categorical) with high utility. (arXiv)

Variational Autoencoders (VAEs) and Their Hybrids

VAEs encode data to a latent space distribution, then decode from latent samples back to synthetic instances.
They can be easier to train and more stable than GANs, but may produce more “blurry” or average-like samples, especially for edge cases.
Some hybrid approaches (e.g., VAE + GAN, VAE with conditional priors) attempt to combine stability with realism.

LLMs / Transformer-Based Models for Tabular or Textual Synthetic Data

Large models (e.g., transformer-style architectures) originally built for text are being adapted to generate structured or semi-structured synthetic data.
One common pattern: convert rows into a serialized token sequence and prompt the model to generate new “rows” consistent with learned patterns.
In domains like healthcare or finance, LLMs can also generate synthetic narratives (clinical notes, transaction descriptions) or synthetic attribute augmentation. (DATAVERSITY)

Auxiliary Models & Nested Synthetic Pipelines

In advanced pipelines, one generative model (auxiliary model) produces synthetic data which is then used to train or test another model. This nested architecture is becoming more common in large-scale AI workflows. (arXiv)
But this layering increases risk: poor synthetic data can cascade errors downstream.

Domain-Specific Enhancements

Knowledge distillation & domain constraints: For example, CK4Gen integrates Cox proportional hazards model knowledge into synthetic survival datasets to preserve hazard ratios and realistic survival curves. (arXiv)
Fairness-aware generation: Enforcing constraints so that protected subgroups are fairly represented and bias amplification is avoided (like Bt-GAN above). (arXiv)

Practical Use-Cases & Real-World Impact

Here are concrete domains and use-cases where synthetic data + generative AI have made notable progress by 2025.

Healthcare & Clinical Research

Synthetic EHR / patient records: Training diagnostic models, decision-support systems, and analytics models without exposing real patient records. (DATAVERSITY)
Clinical trial simulation: Synthetic trial arms, patient recruitment, and outcome simulation when sample sizes are low. (PMC)
Rare disease modeling: For very low-incidence conditions, synthetic augmentation helps build predictive models. (DATAVERSITY)
Survival analysis: CK4Gen (cited above) specifically targets survival datasets while preserving survival curve and hazard distributions. (arXiv)

However, challenges in healthcare are acute. For example:

Synthetic models may miss temporal dynamics or subtle signals, reducing fidelity in critical scenarios (e.g. detecting deterioration). (rgnmed.com)
Over-reliance on synthetic data can lead models to underperform in real-world settings if distribution shift is unaccounted.

Finance, Risk & Fraud Detection

Fraud simulation: Synthetic transaction histories mimic fraudulent behavior to help models detect anomalies, especially when real fraud examples are rare. (Netguru)
Stress testing / scenario generation: Generating hypothetical market or economic scenarios to evaluate models (e.g., shocks, crises) without waiting for real events. (arXiv)
Investment analytics & synthetic factor modeling: Some asset managers use synthetic data to train models where real data is sparse or proprietary. (CFA Institute Daily Browse)
Data sharing among institutions: Banks or financial bodies generate synthetic datasets to share (e.g. in consortiums) to evaluate common risk models while preserving confidentiality. (arXiv)

Other Domains

Market research & consumer analytics: Synthetic consumer responses, simulated survey data to explore “what-if” scenarios while maintaining respondent privacy. (greenbook.org)
Autonomous driving / robotics / simulation: Synthetic sensor and scenario data (e.g., generating rare edge-case scenes) augment real-world collected data. (NVIDIA)
3D, vision, and multimodal pipelines: Synthetic image, video, 3D model generation for training computer vision systems. (NVIDIA)

Business Impact Metrics & Estimates

Gartner estimates that by 2028, 80% of data used for AI will be synthetic in nature. (IBM)
Many organizations report cost savings, faster iteration cycles, and better model robustness by using synthetic data in augmentation pipelines. (Netguru)
However, adoption is still emerging: many organizations are in pilot phases or proof-of-concept stages. (IBM)

Challenges, Risks & Best Practices

Key Risks & Limitations

Fidelity vs. realism trade-off Synthetic datasets often approximate the “central manifold” of distributions and may underrepresent tails or extreme events, limiting generalizability. (rgnmed.com)
Model collapse / recursive degradation If models are trained repeatedly on synthetic data generated by prior models (a form of recursion), performance can degrade over successive generations. This phenomenon is known as model collapse. (Wikipedia)
Privacy leakage / inference attacks Poor synthesis may allow adversaries to reconstruct or memorize real individuals, especially if overfitting occurs. Strong differential privacy or noise constraints are often required.
Bias amplification & fairness issues Synthetic generators may propagate or amplify existing biases (e.g. underrepresented groups), leading to downstream discrimination. Techniques like fairness-aware generation are needed (e.g. Bt-GAN). (arXiv)
Validation and trustworthiness Verifying that synthetic data is “safe” and “useful” is nontrivial. Standard metrics may not reveal subtle errors that hurt downstream performance.
Distribution shift & domain mismatch Synthetic data might not reflect changes in real-world distributions (temporal drift, feature interactions), making models brittle in production.

Best Practices & Guidelines

Here are recommended practices to increase the success rate of synthetic data in production:

Hybrid training: Mix synthetic and real data rather than relying purely on synthetic. This balances realism and flexibility.
Differential privacy / noise injection: Add controlled noise to ensure that synthetic outputs do not leak sensitive records.
Diversity & oversampling rare regions: Force generative models to pay attention to underrepresented regions (e.g. enforce sampling weights).
Domain constraints / rules-based anchoring: Use domain knowledge to enforce invariants (e.g. monotonic relationships, constraints) in generation.
Robust validation suite: Evaluate synthetic data with multiple metrics: distributional similarity (e.g. KL divergence, feature-wise distances), downstream model performance, outlier detection, and sanity checks for edge-case fidelity.
Progressive scaling & sanity checks: Start with small, controlled synthetic sets, validate, then expand.
Governance, audit & transparency: Maintain audit logs, metadata, and clear documentation (when synthetic data was used).
Avoid deep recursion loops: Do not train new generators only on synthetic data without injecting fresh real data to avoid model collapse.
Stress testing on boundary cases: Purposefully generate adversarial or corner-case synthetic samples to test model robustness.
Continuous monitoring after deployment: Monitor for drift, anomalies, or degradation in model performance in real production data.

IBM’s recommendations for synthetic data adoption emphasize many of these, noting that synthetic data is still under-adopted and organizations must build internal capability to validate and monitor utility. (IBM)

Sample Architecture: Synthetic Data Pipeline

Below is a simplified pipeline architecture you might adopt.

Raw / sensitive data  →  Preprocessing & Feature Engineering  
    → Train generative model (GAN / diffusion / transformer)  
    → Synthetic dataset (with metadata, labels)  
    → Validation & filtering  
         • Compare distribution against real  
         • Outlier / anomaly checks  
         • Privacy risk tests  
    → Hybrid dataset (real + synthetic)  
    → Model training / testing / validation  
    → Monitoring & feedback loop

Some pipelines integrate domain-checking modules or rules-based correction steps after generation to maintain consistency (e.g., enforce monotonicity, business rules). For temporal or time-series data, generative models may incorporate autoregressive or recurrent components to capture dynamics.

Outlook & Trends for 2025 and Beyond

Here are some emerging trends and predictions:

Widespread integration of synthetic data in AI pipelines: As generative AI matures, synthetic data becomes a mainstream tool—not just for augmentation, but as a core component of model training. (arXiv)
Transformer-based structured-data models: More LLM-like architectures fine-tuned to generate tabular, time-series, and mixed-type data natively.
On-the-fly synthetic data generation: Real-time synthetic augmentation during model inference or retraining (e.g. live data drift compensation).
Better privacy guarantees: Integration of rigorous differential privacy, federated synthetic generation, and cryptographic techniques to bound leakage.
Self-supervised or self-refining synthetic pipelines: Generative models that improve via closed-loop feedback from downstream tasks.
Domain-specific synthetic platforms: Vertical solutions (finance, life sciences, IoT) offering synthetic generation tuned for domain constraints.
Hybrid generative / refinement approaches: For example, Generative Data Refinement (GDR) methods that "clean" mixed or untrusted data rather than fully synthetic approaches. (A new direction emerging in 2025) (Business Insider)
Guardrails and regulation: As synthetic data use proliferates, regulatory and auditing frameworks will evolve to ensure accountability, fairness, and privacy compliance.

Sample Starter Code Sketch (Python / PyTorch pseudocode)

Below is a rough sketch showing how one might build a simple GAN-based synthetic tabular generator. (This is illustrative—not production-ready.)

import torch
import torch.nn as nn
import torch.optim as optim
 
class Generator(nn.Module):
    def __init__(self, latent_dim, output_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, output_dim),
        )
    def forward(self, z):
        return self.net(z)
 
class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 1),
            nn.Sigmoid(),
        )
    def forward(self, x):
        return self.net(x)
 
def train_gan(real_data, latent_dim=32, epochs=1000, batch_size=128):
    gen = Generator(latent_dim, real_data.shape[1])
    dis = Discriminator(real_data.shape[1])
    opt_g = optim.Adam(gen.parameters(), lr=1e-4)
    opt_d = optim.Adam(dis.parameters(), lr=1e-4)
    criterion = nn.BCELoss()
    for epoch in range(epochs):
        # sample real batch
        idx = torch.randperm(real_data.size(0))[:batch_size]
        real_batch = real_data[idx]
        # train discriminator
        z = torch.randn(batch_size, latent_dim)
        fake = gen(z).detach()
        d_real = dis(real_batch)
        d_fake = dis(fake)
        loss_d = criterion(d_real, torch.ones_like(d_real)) + criterion(d_fake, torch.zeros_like(d_fake))
        opt_d.zero_grad()
        loss_d.backward()
        opt_d.step()
        # train generator
        z2 = torch.randn(batch_size, latent_dim)
        fake2 = gen(z2)
        d_fake2 = dis(fake2)
        loss_g = criterion(d_fake2, torch.ones_like(d_fake2))
        opt_g.zero_grad()
        loss_g.backward()
        opt_g.step()
        if epoch % 100 == 0:
            print(f"Epoch {epoch}, loss_d {loss_d.item():.4f}, loss_g {loss_g.item():.4f}")
    return gen

You would extend this with:

Conditional generation (so you can generate based on labels or categories)
Post-processing & clipping / rounding (to match feature domain)
Privacy noise injection
Validation and rejection sampling

Conclusion

Generative AI–driven synthetic data is rapidly maturing into a core tool in the data scientist’s toolbox. With the right architecture, safeguards, and validation practices, synthetic data enables:

Privacy-preserving model development
Rich augmentation for rare events
Safer data sharing
Faster experimentation

But the hurdles remain: fidelity, bias, model collapse, and validation must be actively managed. As the field evolves in 2025, expect to see:

more robust domain-specific synthetic data platforms
generative models that seamlessly produce structured, multimodal, temporal data
better regulatory frameworks and audit tools

Generative AI and Synthetic Data

What Is Synthetic Data, and Why It Matters#

How Synthetic Data is Created Using Generative Models#

GANs (Generative Adversarial Networks)#

Diffusion Models & Score-Based Generative Models#

Variational Autoencoders (VAEs) and Their Hybrids#

LLMs / Transformer-Based Models for Tabular or Textual Synthetic Data#

Auxiliary Models & Nested Synthetic Pipelines#

Domain-Specific Enhancements#

Practical Use-Cases & Real-World Impact#

Healthcare & Clinical Research#

Finance, Risk & Fraud Detection#

Other Domains#

Business Impact Metrics & Estimates#

Challenges, Risks & Best Practices#

Key Risks & Limitations#

Best Practices & Guidelines#

Sample Architecture: Synthetic Data Pipeline#

Outlook & Trends for 2025 and Beyond#

Sample Starter Code Sketch (Python / PyTorch pseudocode)#

Conclusion#