Synthetic Data for Predictive Modelling

As demand for data-driven insights accelerates, organisations face a growing paradox: the need for richer, more representative datasets, and the simultaneous tightening of privacy, regulatory, and ethical constraints. In domains like healthcare, education, and finance, where data is sensitive and siloed, innovation is often stalled by limited access to usable data.

Synthetic data offers a powerful response. Once considered a stopgap for missing data, it has matured into a strategic enabler—capable of replicating the statistical behaviour of real datasets while sidestepping many of their limitations. When applied responsibly, synthetic data not only preserves privacy but also unlocks new possibilities for model development, performance enhancement, rare event simulation, and secure collaboration.

This guide provides a comprehensive look at the current state of synthetic data. We explore its practical applications, technical foundations, and growing role in machine learning and predictive modelling. Alongside clear benefits, we also examine its limitations, ethical considerations, and the emerging tools and evaluation frameworks that will shape its responsible use in the years ahead.

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Overcoming Common Data Challenges

Synthetic data presents real solutions to some of the most persistent obstacles in data science.

First, it significantly alleviates privacy concerns. In highly sensitive domains like healthcare and education, the use of synthetic data mitigates the risk of re-identification and removes the need for cumbersome de-identification processes. It allows organisations to maintain compliance while still advancing research and innovation.

Second, it improves data availability and scalability. By generating synthetic records that reflect the characteristics of real data, teams can begin building and testing models before real-world data becomes accessible. This has been particularly useful in fields like health tech, where data access delays can stall progress, and in education, where high-quality learner data is often fragmented or inaccessible.

Third, it empowers models to learn from rare events. Whether it’s a once-in-a-decade financial downturn or atypical student behaviour, synthetic data allows these low-frequency, high-impact scenarios to be more fully represented during training, leading to better preparedness and resilience in model outcomes.

Evaluating Performance: Real vs Synthetic vs Hybrid

What’s most striking is the evidence that synthetic data doesn’t just replicate real data—it can sometimes improve on it.

In multiple studies, models trained on synthetic data achieved comparable performance to those trained on real-world datasets. Even more compelling is the consistent success of hybrid datasets, which blend synthetic and real data to leverage the strengths of both. These models not only outperform single-source datasets but also offer greater robustness and reduced overfitting.

For example, in educational settings, a hybrid dataset enabled support vector regression (SVR) to achieve 87.56% prediction accuracy for student performance outcomes—higher than models trained on real or synthetic data alone. In healthcare, logistic regression and SVM models trained on synthetic patient data maintained their predictive power across a range of metrics. And in finance, LSTM models using synthetic time series inputs showed improved forecasting capabilities, particularly for extreme market conditions.

Expanding Strategic Applications

The utility of synthetic data continues to grow in both depth and breadth:

• Education: Predictive models informed by synthetic student data help institutions identify at-risk learners earlier and deliver tailored interventions.

• Healthcare: Synthetic patient records facilitate external model tuning, testing, and validation—without breaching confidentiality or delaying projects.

• Finance: Quantum-enhanced models use synthetic datasets to model volatility and black swan events, helping improve risk management.

• Model Optimisation: For hyperparameter tuning, synthetic data provides a privacy-preserving environment to fine-tune model configurations before applying them to sensitive real-world datasets.

Behind the Scenes: How Synthetic Data is Built

The power of synthetic data lies in the technologies behind it. Tools like Generative Adversarial Networks (GANs), Conditional Tabular GANs (CTGAN), RealTabFormer, and Quantum Wasserstein GANs (QWGANs) are used to generate high-fidelity data that retains the statistical structure of the original. These models learn to capture correlations, data distributions, and even temporal patterns in time series.

Feature engineering further enhances these capabilities. Selecting the right mix of input features—especially when drawing from both real and synthetic sources—can lead to substantial improvements in model accuracy and generalisability. Studies also show that the inclusion of well-balanced synthetic datasets supports more effective feature selection and reduces the impact of data imbalance.

How Much Real Data Do You Need?

One of the more common questions facing organisations is: how much real data is required to generate useful synthetic datasets? There is no single threshold. The answer depends on the complexity, diversity, and intended application of the data. However, case studies across industries reveal useful benchmarks.

• In education, as few as 330 real student records were used to generate 5,000 synthetic records. This approach proved effective for predicting learner performance.

• In healthcare, a much larger sample—over 58,000 patient records from the MIMIC dataset—was used. The resulting synthetic data maintained similar class distributions and showed comparable predictive accuracy to real data.

• For financial time series, daily S&P 500 data over an eight-year span formed the training base. Though no exact row count was given, the dataset's size and variability were essential in modelling both regular patterns and extreme market events.

What these examples reveal is that sheer volume is less important than representativeness. If the source data is diverse, includes rare or extreme events, and reflects the intricacies of the use case, synthetic data can be highly effective—even in modest quantities.

Capturing Complexity: Techniques and Strategies

High-quality synthetic data requires more than just generative models; it demands thoughtful data preparation, robust engineering, and evaluation throughout the process.

Advanced Generative Architectures

In terms of architecture, Adaptive Conditional Tabular GANs (ACTGAN) have been used to generate privacy-preserving student data. Transformer-based models like RealTabFormer have shown particular strength in maintaining class balance in health data scenarios. CTGAN, a well-established model for tabular data, continues to be used for its strong baseline performance.

In more cutting-edge work, quantum-enhanced models like Quantum Wasserstein GAN with Gradient Penalty (QWGAN-GP) have been applied to time series data. These models utilise parameterised quantum circuits to generate statistically similar sequences of market behaviour. Experiments with QWGAN-GP were conducted via the qBraid SDK and platform, which provided access to quantum hardware, simulators, and tooling like PennyLane, Qiskit, and Amazon Braket.

Data Preprocessing and Transformation

Preprocessing remains critical to help ensure that synthetic time series data reflected realistic volatility, correlations, and lagged effects. Examples include:

• Transforming index prices to logarithmic returns.

• Normalising to zero mean and unit variance.

• Applying an inverse Lambert-W transform to correct heavy-tailed distributions.

• Using rolling windows to capture sequential patterns.

Limitations, Challenges, and Mitigations

Limitations and Technical Barriers

While synthetic data continues to gain traction as a strategic tool, it is not without its limitations. These challenges span technical, operational, and conceptual dimensions, and they must be carefully considered to ensure responsible use.

Fidelity and Representational Challenges

Synthetic datasets may fall short in fully capturing the intricate dynamics of real-world systems, particularly in datasets with high-cardinality categorical variables or complex temporal relationships. Additionally, some models, especially those reliant on subtle statistical cues, may experience a drop in performance if synthetic data is not generated with sufficient precision.

Common limitations include:

• Smoothing of peaks and underestimation of shorter stays in distributions.

• Difficulty replicating phenomena like volatility clustering or extreme market behaviours.

• Loss of subtle statistical cues, which can impact model accuracy for sensitive applications.

Performance Limitations

The utility of synthetic data depends heavily on how well it supports predictive modelling. Key concerns include:

• Oversampling rare events in small volumes may fail to improve, or even degrade, prediction accuracy.

• Class imbalance in synthetic data can lead to poor performance in models like XGBoost, especially in recall and specificity.

• Models reliant on nuanced distributions may underperform if fidelity is lacking.

Quantum and GAN-Specific Constraints

Quantum-based models like QWGANs offer exciting possibilities for improving the realism of synthetic data but currently face practical constraints due to the limitations of today’s quantum hardware. That said, research is progressing rapidly in this space, and new methods—including diffusion models and improved privacy-preserving techniques—are opening new pathways forward.

Technical barriers include:

• Training instability, vanishing gradients, and mode collapse.

• Hardware limitations in the current NISQ (Noisy Intermediate-Scale Quantum) era, including low qubit counts, high noise levels, and short coherence times.

• Integration challenges when encoding classical data into quantum systems or decoding synthetic quantum outputs back into usable formats.

• Computational intensity and infrastructure demands, even for hybrid quantum–classical models.

Even when the generator is classical, training stability and computational requirements are significant. Hybrid approaches—combining quantum generators with classical discriminators—are one practical compromise, but they still require substantial infrastructure and expertise.

Scalability and Resource Demands

Synthetic data generation, particularly for time series or complex multivariate datasets, can be computationally expensive. This may limit its adoption in resource-constrained environments or organisations without specialist infrastructure.

Adoption and Trust Barriers

Despite growing interest, skepticism persists—especially in regulated sectors such as healthcare—due to:

• Lack of standardised evaluation frameworks.

• Evolving regulatory expectations around synthetic data’s use under laws like HIPAA and PIPEDA.

• Concerns about reliability, explainability, and fairness in decision-making based on synthetic data.

Limits on Generalisability

Findings derived from synthetic data are only as robust as the source data they are based on. Models trained on regional or narrowly scoped data (e.g., students from one region or specific market conditions) may not generalise well to broader populations or different environments.

Tackling Domain-Specific Data Challenges

Class Imbalance

In health data, models like XGBoost performed poorly with imbalanced synthetic datasets, while SVM and logistic regression were more resilient. This underscores the need to match data preparation and model selection.

Under-represented Extreme Events

In finance, simply adding a small number of synthetic extreme events didn’t improve model accuracy. More effective was the “feature method,” which used synthetic series to represent alternative market trajectories.

Generalisability Across Conditions

Models like QWGAN-GP must be trained on diverse, representative datasets to ensure predictive accuracy across time periods and market scenarios. Including period-specific features or adapting loss functions during training can further improve performance.

Mitigating Challenges in GAN Training

Training GANs—especially their quantum variants (QGANs)—comes with unique technical challenges:

• Mode Collapse and Vanishing Gradients are common pitfalls. Wasserstein GANs with gradient penalties help mitigate these by enforcing the Lipschitz constraint.

• Training Stability remains a core issue. Hybrid quantum–classical architectures (quantum generator + classical discriminator) improve convergence.

• Hardware Limitations of current quantum systems (NISQ era) affect coherence times, error rates, and scalability. Solutions include efficient circuit design and error mitigation strategies.

• Quantum-Classical Data Integration presents a workflow challenge, as classical data must be encoded into quantum states—or decoded from quantum outputs—for model interaction.

Evaluating Synthetic Data: Fidelity and Utility in Practice

Two dimensions define synthetic data's quality: fidelity and utility.

Fidelity refers to how closely synthetic data replicates the statistical structure of real-world data. Evaluation methods include comparing univariate distributions, class ratios, bivariate correlation matrices, and principal component structures. Visual tools like QQ plots, PDFs, and CDFs support this, along with quantifiable metrics like Wasserstein distance and entropy.

In time series modelling, autocorrelation functions, dynamic time warping (DTW), and lagged scatter plots are useful for confirming temporal consistency. A synthetic data quality score—often a composite of field distribution, field correlation, and deep structure metrics—can provide a summary evaluation.

Utility, on the other hand, is measured by the synthetic data’s ability to train effective machine learning models. Performance metrics like accuracy, recall, specificity, precision, and F1 score are compared between models trained on real, synthetic, and hybrid data. In educational applications, hybrid datasets have achieved prediction accuracy up to 87.76%. In finance, using synthetic features to augment real data has improved RMSE and MAE by over 30% in some cases, including for rare-event prediction.

High-performing models validated their synthetic data using measures such as:

• Field distribution and correlation stability.

• Deep structure stability (ACTGAN scored 85%).

• Very low Wasserstein distances between synthetic and original data (e.g., 0.00086 for QWGAN-GP).

Enhancing Model Performance with Synthetic Data

Multiple studies found that predictive models trained on synthetic data alone could match real-data-based models in accuracy. However, the most notable gains came from hybrid datasets:

• In education, a hybrid dataset using ACTGAN outputs and real data yielded a top prediction accuracy of 87.76%.

• In finance, incorporating synthetic data as a feature led to 36.2% and 39.4% improvements in RMSE and MAE respectively for general predictions, and a 33.1% gain for extreme event predictions.

• For healthcare, synthetic data proved highly useful for hyperparameter tuning, enabling performance optimisation without exposing sensitive data.

Generator Comparisons

While few direct benchmarks were presented, some relative observations stand out:

• In healthcare, RealTabFormer slightly outperformed CTGAN for hyperparameter tuning (Accuracy: 0.89 vs. 0.86; F1 Score: 0.87 vs. 0.75).

• ACTGAN showed strong fidelity in education contexts but wasn’t directly compared to other models.

• QWGAN-GP demonstrated high similarity to the original S&P 500 dataset and significant performance gains when used in LSTM models.

Why Synthetic Data Offers More Than Privacy

For many, synthetic data’s initial appeal lies in its ability to reduce privacy risks. But the strategic value extends far beyond compliance.

In domains where data is sensitive or siloed—like healthcare, education, and finance—synthetic data enables innovation without the delays of governance approval cycles. Teams can begin development, modelling, and experimentation before real data is available. Synthetic datasets also enable organisations to test ideas more safely, simulate rare events, and explore edge cases that aren’t well represented in real data.

Synthetic data supports cross-border and external collaborations. When real data cannot leave a protected environment, synthetic versions of that data can be used to engage with external partners, conduct hyperparameter tuning, or scale development workflows.

In the financial sector, synthetic time series have helped improve forecasting models for extreme events. In education, synthetic datasets have improved risk models that identify at-risk students. And in healthcare, synthetic data has enabled diagnostics and treatment recommendations without violating privacy or regulatory rules.

Ethical Implications: More Than a Technical Challenge

While privacy preservation is often positioned as synthetic data's ethical advantage, several less obvious issues must be considered.

A central concern is fidelity. If synthetic data smooths over outliers or fails to capture phenomena like volatility clustering, models built on it may deliver biased or misleading results. For example, underestimating hospital stay durations or financial risks could lead to poorly calibrated interventions.

Bias is another risk, particularly if class imbalance in the original data is replicated—or exacerbated—in the synthetic version. In one study, models trained on imbalanced synthetic health data suffered reduced recall and specificity, especially when using tree-based methods like XGBoost.

Generalisability is also a challenge. If a synthetic dataset is derived from a narrow population (e.g., students from one state or patients from one hospital), applying that model to a broader population may lead to ineffective or even harmful results.

Finally, the lack of standardised evaluation frameworks makes it difficult to audit or benchmark synthetic data quality. This limits confidence in the outputs and presents an ethical challenge in fields where decisions affect lives, such as healthcare or criminal justice.

Final Thoughts

Synthetic data is rapidly evolving from a compliance workaround into a strategic enabler of innovation. With the right approaches—advanced generation models, robust pre-processing, hybrid datasets, and clear performance validation—organisations can overcome many of the limitations tied to real-world data.

At Fry & Laurie Consulting, we help clients identify when and how synthetic data can be integrated into their data strategies. Whether you are constrained by privacy regulations, facing limited data availability, or exploring new frontiers in predictive analytics, synthetic data offers a path forward.

If you're curious about implementing synthetic data in your organisation, let’s have a conversation about what's possible.