AI-Generated Synthetic Data Is New. Curious? We’ve Got Answers.

Synthetic data is artificially generated data that mimics the structure and statistical properties of real-world data, but is not derived from actual events or individuals. It is often used when real data is difficult to collect, limited in scope, or subject to privacy regulations.

While the concept of synthetic data has been around for many years, it was traditionally rule-based. In that approach, users manually defined parameters for data generation, such as "create a whole number between 100 and 1,000 following a normal distribution." This method works for simple use cases but lacks realism and complexity.

Today, synthetic data typically refers to AI-generated synthetic data, which is created using advanced generative models. These models learn from real datasets to reproduce realistic, high-dimensional data that preserves statistical patterns and relationships—without containing any personally identifiable information (PII). The result is data that looks and behaves like the original but is fully privacy-safe and compliant.

Synthetic data can be generated using a variety of techniques, depending on the goals of the project and the type of data required. The most common methods include:

1. Statistical or Rule-Based Generation: These traditional methods rely on predefined rules or statistical properties to create data. For example, you might generate values following a normal distribution or replicate a dataset’s mean and variance. While simple and fast, this approach is limited in its ability to capture complex relationships in real-world data.

2. Machine Learning and Generative Models: Modern synthetic data is most often generated using advanced machine learning techniques. Models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other deep generative models are trained on real datasets and learn to reproduce their structure, relationships, and distributions.

GANs, for instance, use two neural networks: a generator that creates synthetic data and a discriminator that tries to distinguish it from real data. Through iterative training, the generator becomes highly effective at producing data that is statistically and structurally indistinguishable from the original.

3. Agent-based Modeling: This method simulates the interactions of individual entities (agents), each with defined behaviors and decision rules. It is particularly useful for modeling complex, dynamic environments like financial markets or urban mobility systems.

4. Simulation-based Approaches: These techniques involve building computational models of systems, such as weather, traffic, or logistics, and using them to generate synthetic data. The output is based on theoretical models or known system behaviors.

Synthetic data is used across a wide range of domains where access to real data is limited, sensitive, or insufficient. The three most common use cases include:

1. Artificial Intelligence (AI) and Machine Learning (ML): Synthetic data is widely used to train and validate machine learning models, especially when real-world data is scarce, imbalanced, or sensitive. It helps address challenges such as missing labels, underrepresented classes, and privacy constraints. In reinforcement learning, synthetic environments provide agents with simulated interactions that accelerate training without the need for costly real-world experimentation.

2. Data Sharing and Collaboration: Because synthetic data preserves the statistical properties of real data while removing personal identifiers, it enables privacy-compliant data sharing. This is particularly valuable in regulated industries like healthcare and finance, where organizations can share synthetic datasets for research, analytics, or open innovation without risking sensitive information.

3. Software Development and Testing: Synthetic data supports the testing of applications, databases, and data pipelines under realistic conditions. It allows teams to simulate high-load scenarios, edge cases, and user behaviors—ensuring robust functionality and performance without exposing real user data.

The key benefits of synthetic data include:

Privacy Protection: Since synthetic data contains no real personal information, it eliminates the risk of privacy breaches.

Improved Data Availability: It can be generated on demand, even when real data is unavailable, incomplete, or expensive to collect.

Customizable Scenarios: Developers and data scientists can control data characteristics to simulate specific features, anomalies, or correlations.

Bias Mitigation: Carefully generated synthetic data can help reduce bias by ensuring balanced representation across classes or demographic groups.

Several industries are actively leveraging synthetic data to overcome data privacy constraints, improve access to high-quality data, and accelerate innovation:

Finance and Banking: Synthetic data enables secure testing and validation of fraud detection systems, credit scoring models, and risk assessments—without exposing sensitive transaction or customer data. It also supports data sharing across departments or with external vendors in a privacy-compliant way.

Insurance: Insurers use synthetic data to model risks, predict claims, and test pricing strategies without relying on real policyholder data. It also helps train machine learning models for fraud detection and simulate new insurance product rollouts under realistic data conditions.

Healthcare: Due to strict patient privacy requirements, synthetic data is especially valuable in healthcare. It allows for the development and sharing of patient-like datasets for disease research, clinical model training, diagnostics, and treatment prediction, all while preserving patient confidentiality.

Retail and E-commerce: Due to strict patient privacy requirements, synthetic data is especially valuable in healthcare. It allows for the development and sharing of patient-like datasets for disease research, clinical model training, diagnostics, and treatment prediction, all while preserving patient confidentiality.

Public Sector: Governments and public institutions use synthetic data to simulate populations, test policy scenarios, and enable safe inter-agency data sharing. It also supports transparency initiatives by allowing the release of realistic datasets without privacy risks.

Across all these industries, the key advantage of synthetic data is its ability to deliver realistic, privacy-safe, and purpose-built data that fuels innovation while avoiding the limitations and risks associated with real-world data.

Global payments network SWIFT provided synthetic datasets representing data held by the SWIFT payments network and datasets held by partner banks for the U.S. PETs Prize at the end of 2022.

US Health Insurer Humana has published synthetic member records on their Humana Data Exchange (HDX) as a way to accelerate innovation in healthcare. It is targeted towards software developers, data scientists, and product team stakeholders in the healthcare industry and allows them to train machine learning models faster.

J.P. Morgan uses synthetic data to accelerate the development of AI solutions and to enable collaboration with the academic community. Synthetic data generation allows them to think, for example, about the full lifecycle of a customer’s journey that opens an account and asks for a loan. They’re not simply examining the data to see what people do, but they’re also able to analyze their interaction with the firm and essentially simulate the entire process.

After generating the synthetic data, it's important to validate it against the real-world data. This can involve comparing the statistical properties of the synthetic data with those of the real data. Most frameworks and vendors will provide some sort of Quality Assurance report that gives a first overview of the quality of the created synthetic data.

The real benchmark though, is testing the performance of models trained on the synthetic data using a validation set of real data. This concept known as Train Synthetic Test Real (TSTR) gives the real answer, if a synthetic dataset can really be used as a substitute for real data. If you’re further interested in exploring this concept we have prepared a Jupyter Notebook on Google Colab that gives a practical overview how to do this.

One of the main challenges with synthetic data is ensuring that it accurately represents the real-world data it's intended to mimic. If the synthetic data doesn't capture the key characteristics, correlations, and variability present in the real data, models trained on this data might not perform well in real-world applications.

There's a risk that models trained primarily on synthetic data might overfit to the characteristics of the synthetic data and underperform on real-world data. This is particularly a concern if the synthetic data oversimplifies the problem or misses certain complexities present in the real-world data.

The good news is that there is an easy to use platform for all your synthetic data generation needs, that has proven to generate synthetic data of highest quality and accuracy: the MOSTLY AI Platform. But don’t take my word for it - head to our FREE version, sign up and see for yourself!

With increasing data privacy regulations, synthetic data offers a compelling solution to many data-sharing challenges. When synthetic data is generated correctly, it mimics the statistical characteristics of the original data without containing any actual personal information. This allows the synthetic data to be used for a wide range of purposes, including research, development, and analysis, without violating privacy laws or regulations.

For organizations that need to share data with third parties, synthetic data can be a game-changer. Instead of sharing actual customer or user data, which could risk violating privacy regulations, organizations can share synthetic data that has the same statistical properties. This allows third parties to conduct meaningful analysis without having access to sensitive information.

Additionally, synthetic data can facilitate collaboration between different organizations. For example, in healthcare, hospitals or research institutions may want to collaborate and share patient data for research purposes. However, due to privacy regulations, sharing real patient data can be challenging. In this scenario, synthetic data that maintains the statistical properties of the real patient data can be shared instead, enabling collaborative research while ensuring patient privacy.

Furthermore, synthetic data can also make open data initiatives more feasible. Governments and organizations can generate and release synthetic datasets for public use without exposing any sensitive information. This can spur innovation and allow more researchers and developers to leverage the data.

Synthetic data is not automatically private. Several measures need to be taken during the synthetic data generation process to make sure that this is a privacy preserving process.

Techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) can generate synthetic data that statistically resembles the original data but does not directly replicate unique data points. In designing these models, special care must be taken to avoid reproducing identifiable patterns from the original data (= overfitting of the models) that could lead to re-identification of individuals.

In addition one aspect that needs to be taken care of is how outliers are handled. Sophisticated synthetic data generation tools like the MOSTLY AI Synthetic Data Platform have in-built mechanisms that handle various outliers such as extreme values for numerical data or rare categories for categorical data.

Validation of synthetic data is another key step. This involves testing to ensure that the synthetic data does not contain sensitive information. Re-identification tests can be performed, where attempts are made to match records in the synthetic data back to known records in the original data. If a significant number of records can be matched, this may indicate that the synthetic data is revealing sensitive information.

Finally, strong governance and oversight are essential. This includes clear policies on how synthetic data is generated, used, and shared, and who has access to the original and synthetic data.

The impact of synthetic data on the performance of AI models in real-world settings depends largely on the quality of the synthetic data.

The quality and relevance of synthetic data play a huge role. If synthetic data accurately captures the statistical properties and variability of the real-world data, it can be a valuable asset for training AI models and in practice lead to models of almost similar quality as if trained on real world data.

In certain cases synthetic data can even enhance model performance by providing more diverse and balanced training data, especially for classes or scenarios that are underrepresented in the real data.

However, if the synthetic data does not adequately represent the real-world conditions, it may lead to models that perform poorly in real-world settings. This can happen if the synthetic data oversimplifies the problem or misses important features or correlations present in the real data. Models trained on such synthetic data may overfit to the characteristics of the synthetic data and underperform on real data.

Synthetic data can have an even more profound impact on real-world settings: in cases where there simply is no real-world data to train AI models. Oftentimes real-world data is difficult or costly to obtain, or privacy concerns limit the use of real data. In these cases synthetic data - even if not perfect - is much better than having no data at all.

The major downside of using synthetic data to train AI models is the fact that synthetic users, scenarios, or behaviors do not correspond to real individuals or events. Although synthetic data can be engineered to statistically mirror aspects of real-world data almost perfectly, it's important to remember that each synthetic instance is essentially a fabrication, not tied to a specific real-world counterpart.

For example, if an AI model trained on synthetic data identifies a pattern or behavior that seems actionable, it's not possible to directly engage with the synthetic users exhibiting that behavior, because they don't exist. Instead, one would need to find corresponding patterns or behaviors in the real-world data. This means an extra step is required: transferring the insights generated on synthetic data back to the real world data.

Furthermore, if the synthetic data does not adequately capture the complexity and diversity of the real world, there can be a discrepancy between how the model performs in the synthetic environment versus how it performs when deployed in the real world. This could potentially lead to models that perform well on synthetic data but fail to generalize effectively to real-world data.

That’s why it is important to make sure that the synthetic data used for training AI models is really as accurate and statistically representative as possible.

Synthetic data has the potential to greatly enhance innovation and cooperation across organizations. Traditionally, sharing data between organizations, especially those in sensitive fields like healthcare or finance, has been fraught with challenges due to privacy concerns and regulatory constraints. Synthetic data provides a way around these issues, enabling more free and open data sharing.

By enabling data sharing without privacy risks, synthetic data can allow organizations to collaborate on common problems or research projects. For example, multiple hospitals could share synthetic patient data to jointly develop AI models for predicting disease outcomes or optimizing treatment strategies. This type of cooperation could lead to faster advancements and more robust solutions than if each organization worked separately with its own limited dataset.

In addition, synthetic data can be shared more freely for open data initiatives or public challenges. For example, a government agency could release synthetic datasets that mimic real-world conditions, enabling researchers and developers to build and test solutions for public issues. This can spur innovation by providing a broader community with access to relevant data.

Synthetic data can foster cooperation between organizations and the AI research community. Organizations can release synthetic datasets derived from their proprietary data, allowing researchers to develop new algorithms and techniques that can benefit the organization. In return, the researchers get access to realistic datasets that can drive their research.

Bias in AI and machine learning often stems from the data used to train the models. If the training data is not representative of the problem space or population, the model can develop biased predictions. Synthetic data can help mitigate such biases in several ways.

Firstly, synthetic data allows for controlled data generation. This means one can generate a dataset that accurately represents different classes, scenarios, or populations that might be underrepresented in the real data. For instance, if a certain demographic is underrepresented in the real data, more synthetic data representing that demographic can be generated to ensure a balanced dataset.

Secondly, synthetic data can be used to generate data for scenarios that are rare or hard to capture in the real world but are important for training the model. For example, in autonomous vehicle development, synthetic data can simulate rare but critical situations, such as certain types of accidents or extreme weather conditions. This ensures the model is trained on these scenarios and can handle them appropriately.

Moreover, synthetic data can be used to understand the impact of bias in the models. By generating synthetic data with known biases and feeding this data to the model, one can observe how these biases affect the model's performance. This can provide valuable insights into how the model might behave when exposed to biased real-world data and guide the development of strategies to mitigate these biases.

We have written an entire blog series on Fairness and Bias that can be found here.

When you are using a powerful tool like the MOSTLY AI Data Intelligence Platform, the goal of the tool is to create synthetic data that is as close as possible to the real data without compromising the privacy of the original dataset.

This means that any bias that existed in the original dataset will also be reflected in the synthetic dataset - and this is a feature, not a bug. All the measures that are in place to make sure that the synthetic data is highly representative of the original data will naturally also make sure that existing biases are retained. However, these measures will also make sure that no new biases will be added during the synthesization process.

In practice you can and should verify this by comparing the original dataset and the synthetic dataset with regards to the biases found and for example by comparing the performance of ML models that were trained on each of these datasets.

Promoting transparency and accountability when using synthetic data in AI systems involves several crucial steps. The process starts with clear documentation of how the synthetic data was generated or what tools were used. The documentation should also describe any known limitations or biases of the original and synthetic data.

When synthetic data is used to train AI models, it's important to clearly state this in any reports or publications about the AI system. This allows users, reviewers, and auditors to properly interpret the results of the AI system.

Furthermore, when synthetic data is used in place of real data due to privacy concerns, it's crucial to validate that the synthetic data does not contain any sensitive information. This involves implementing robust privacy-preserving mechanisms in the data generation process or working with a trusted vendor to create the synthetic data.

One of the key aspects of responsible AI is ensuring data privacy and maintaining user trust. Synthetic data, which doesn't include real personal information, can uphold this principle. It allows organizations to bypass many of the privacy concerns associated with real-world data, enabling them to test and train AI models without the risk of exposing sensitive information.

Transparency in AI involves making clear to stakeholders how an AI system works and makes decisions. When synthetic data is used, it's crucial to clearly document the process by which the use of synthetic data was implemented.

Moreover, synthetic data can help promote robustness and fairness in AI systems. Since synthetic data can be generated in large quantities and designed to represent a wide range of scenarios, it can be used to test AI systems under various conditions and edge cases. This helps ensure that the AI system performs well across diverse situations, contributing to a more robust and fair system.

Finally, using synthetic data can help with replicability in AI research. Since synthetic data can be freely shared without privacy concerns, it allows others (e.g. a validation group within an organization) to reproduce experiments and verify results, which is a key aspect of trust and transparency in a system.

The future of synthetic data is likely to be characterized by technological advancements, regulatory developments, broad industry adoption, and a tighter integration with AI development.

Improved Generation Techniques: As research progresses, we can expect to see more advanced techniques for generating synthetic data. This might include more sophisticated generative models that can create increasingly realistic and diverse synthetic data, or new techniques for ensuring privacy in synthetic data.

Regulation and Standards: As the use of synthetic data becomes more widespread, we might see the introduction of regulations and standards related to synthetic data. This could include standards for how synthetic data should be generated and used, or regulations to ensure that the use of synthetic data respects privacy and ethical considerations.

Broad Adoption Across Industries: As organizations become more aware of the benefits of synthetic data, we're likely to see increased adoption across a variety of industries and different sizes of organizations.

At MOSTLY AI we are constantly pushing the boundaries of what’s possible to make sure that synthetic data becomes the standard of how organizations work with and share data.