The following guest article was submitted by Mikheil Shengelia, Research Analyst at Eagle Alpha, an alternative data aggregation platform providing supporting advisory services for data buyers and vendors.
For decades, financial markets have operated under a paradox: they are among the most data-rich environments in the world, yet access to usable, high-quality data remains constrained. Much of the most valuable data is locked behind regulatory barriers, internal silos, or commercial agreements. Synthetic data fundamentally alters this dynamic by shifting the paradigm from data collection to data generation. Instead of relying solely on observed datasets, financial institutions can now engineer datasets that replicate the statistical behavior of real-world systems while avoiding direct exposure to sensitive or proprietary information.
This shift is not merely technical; it represents a deeper transformation in how financial institutions conceptualize data as an asset. Data is no longer just something to be acquired and protected, but something that can be modeled, extended, and strategically manufactured. Synthetic data sits at the center of this transformation, enabling institutions to bypass traditional bottlenecks while introducing new layers of complexity in validation, governance, and ownership.
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The Technical Foundations of Synthetic Data
Synthetic data generation relies on a range of techniques, from relatively simple statistical sampling to advanced machine learning models. At a foundational level, synthetic data generators aim to replicate distributions, correlations, and structural relationships within datasets while ensuring that individual records cannot be traced back to real entities.
Modern approaches increasingly rely on generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These models are capable of capturing high-dimensional relationships within financial datasets, including nonlinear dependencies and temporal structures.
Figure 1: Synthetic Data Generation Methods (Source: Arthur D. Little)
In time-series finance, which includes trading data, credit histories, and macroeconomic indicators, preserving temporal structure is critical. Synthetic data models must replicate not just static distributions but also dynamic behaviors such as volatility clustering, autocorrelation, and regime shifts.
This requires sophisticated modeling techniques that go beyond simple resampling. For example, GAN-based approaches can be adapted to generate realistic financial time series, while agent-based simulations can model interactions between market participants to produce emergent patterns. According to the Alan Turing Institute, synthetic data generators must replicate these statistical features while producing entirely new observations, enabling both realism and privacy.
The concept of “utility versus privacy” is central to synthetic data generation. High-fidelity synthetic data closely resembles real data and is highly useful for modeling, but it also increases the risk of information leakage. Conversely, highly privatized synthetic data may be safer but less useful. Balancing these competing objectives is one of the core technical challenges in the field, and it has direct implications for both regulatory compliance and intellectual property protection.
Applications in Capital Markets
In capital markets, synthetic data is increasingly used to support trading strategies, portfolio construction, and market simulation. One of its most powerful applications is in scenario generation. Historical market data is inherently limited because it only reflects events that have already occurred. Synthetic data allows traders and risk managers to simulate hypothetical scenarios, including extreme events that have no historical precedent.
This capability is particularly relevant in derivatives markets, where pricing models often depend on assumptions about volatility and correlation structures. By generating synthetic paths for underlying assets, institutions can test the robustness of pricing models and hedging strategies under a wide range of conditions. Similarly, portfolio managers can use synthetic data to evaluate how portfolios might perform under different macroeconomic scenarios, including interest rate shocks, geopolitical events, or liquidity crises.
Algorithmic trading firms also benefit from synthetic data by using it to train and backtest models. Traditional backtesting relies on historical data, which can lead to overfitting and limited generalization. Synthetic datasets can introduce variability and reduce dependence on specific historical patterns, leading to more robust models. However, this also introduces new risks, particularly if the synthetic data fails to capture critical market dynamics or introduces unrealistic artifacts.
Applications in Banking, Credit, and Insurance
Beyond capital markets, synthetic data is widely applicable across banking, credit, and insurance. In credit risk modeling, for example, synthetic data can be used to generate diverse borrower profiles, including rare or underrepresented segments. This helps improve the fairness and accuracy of credit scoring models by reducing bias and increasing coverage. It also allows institutions to test how models perform under changing economic conditions, such as rising unemployment or inflation.
In retail banking, synthetic data is often used to simulate customer behavior, including transaction patterns, product usage, and channel interactions. This enables banks to develop and test personalization strategies without exposing real customer data. In insurance, synthetic datasets can be used to model claims scenarios, assess underwriting risks, and simulate catastrophic events. These applications are particularly valuable in areas where historical data is sparse or highly skewed.
Fraud detection remains one of the most mature use cases. Synthetic data can be used to generate realistic fraud scenarios, including evolving tactics used by malicious actors. This allows institutions to stay ahead of threats by continuously updating their detection models. It also reduces reliance on historical fraud cases, which may not fully represent emerging risks.
Data Monetization and Synthetic Products
Synthetic data is also reshaping how financial data is monetized. Traditional data monetization models rely on selling access to raw datasets, often under restrictive licensing agreements. Synthetic data enables more flexible models by decoupling data utility from data ownership. Providers can create synthetic datasets that retain analytical value while minimizing legal and competitive risks.
This opens the door to new types of data products. For example, a provider might offer a synthetic dataset that captures consumer spending trends without revealing individual transactions or merchant identities. Similarly, a hedge fund might develop proprietary synthetic datasets as part of its internal research process, effectively creating new forms of alpha generation. Over time, this could lead to the emergence of synthetic data marketplaces, where datasets are traded in a form that is inherently more shareable and scalable than traditional data products.
However, this also raises questions about commoditization. If synthetic data becomes widely available, the uniqueness of underlying datasets may be diminished. The differentiation may shift toward the quality of the generation models, the richness of the underlying data, and the ability to integrate synthetic data into decision-making processes.
Intellectual Property: Ownership, Derivation, and Control
Intellectual property is one of the most complex and strategically important aspects of synthetic data in finance. Unlike traditional datasets, which have relatively clear ownership structures, synthetic data exists in a layered ecosystem of rights and dependencies. The original dataset, the model used to generate synthetic data, and the resulting synthetic dataset may each have different owners and legal statuses.
One of the central questions is whether synthetic data constitutes a derivative work. If it is considered derivative, then the rights of the original data owner may extend to the synthetic dataset. This could limit the ability to distribute or monetize synthetic data without explicit permission. However, if synthetic data is deemed sufficiently distinct, it may be treated as a new and independent dataset, with its own ownership rights. The distinction often hinges on technical factors such as the degree of similarity between the synthetic and original data, as well as legal interpretations that are still evolving.
Another critical issue is the role of third-party vendors. Many financial institutions rely on external providers to generate synthetic data, either through software platforms or managed services. In such cases, contracts must clearly define ownership rights, usage permissions, and liability. Questions arise regarding whether the vendor retains any rights to the generated data, particularly if their models are trained on multiple client datasets. This creates potential risks around data leakage and cross-client contamination.
Control over synthetic data also extends to downstream usage. Even if ownership is clearly defined, there may be restrictions on how synthetic data can be used, shared, or resold. These restrictions are often embedded in licensing agreements and are becoming increasingly sophisticated as organizations seek to protect their intellectual property while enabling broader data utilization.
Privacy, Compliance, and Regulatory Considerations
Synthetic data is often positioned as a solution to privacy challenges, but its regulatory status is not straightforward. While synthetic data can reduce the risk of exposing personal information, it is not automatically exempt from data protection laws. Regulators are increasingly focused on the concept of “re-identification risk,” which refers to the possibility that synthetic data could be used to infer information about real individuals.
Financial regulators are also concerned with issues such as model transparency, auditability, and data lineage. Institutions must be able to demonstrate how synthetic data is generated, how it is used, and how risks are managed. This requires robust governance frameworks that integrate technical, legal, and operational considerations.
In some jurisdictions, synthetic data is being explicitly addressed in regulatory guidance, while in others it remains an emerging topic. This creates a fragmented landscape in which organizations must navigate varying requirements across regions. As synthetic data becomes more widely adopted, it is likely that regulatory frameworks will become more standardized, but this process is still in its early stages.
Risks, Limitations, and Model Dependencies
Despite its potential, synthetic data introduces new forms of risk that must be carefully managed. One of the most significant is model risk. Synthetic data is only as good as the models used to generate it, and any flaws in those models can propagate into downstream applications. This includes issues such as bias, overfitting, and the failure to capture critical relationships within the data.
Another concern is the potential for “model collapse,” particularly in environments where synthetic data is used recursively. If models are trained on synthetic data that was itself generated from other synthetic datasets, the quality and diversity of the data may degrade over time. This can lead to a loss of realism and reduced effectiveness in real-world applications.
Validation is a persistent challenge. Organizations must develop methods to assess whether synthetic data accurately reflects the underlying phenomena it is intended to represent. This involves comparing statistical properties, testing model performance, and evaluating potential risks of information leakage. These processes are often complex and resource-intensive, requiring specialized expertise.
Conclusion
Synthetic data represents a structural shift in how financial institutions approach data. By enabling the creation of realistic, privacy-preserving datasets, it unlocks new capabilities across trading, risk management, credit modeling, and fraud detection, while also redefining data as a dynamic, manufacturable asset. At the same time, its growing role in data monetization introduces new competitive dynamics, where differentiation increasingly depends not on exclusive access to raw data, but on the sophistication of generation models and the ability to translate synthetic outputs into actionable insight.
However, this transformation comes with significant complexity. Questions around intellectual property, ownership, and derivation remain unresolved, while regulatory scrutiny continues to evolve alongside the technology. Moreover, synthetic data introduces its own risks—particularly around model dependency, validation, and the potential degradation of data quality over time. As adoption of synthetic data accelerates, success will depend on robust governance frameworks that balance utility, privacy, and control, ensuring that synthetic data enhances decision-making without undermining trust, compliance, or long-term data integrity.
In Eagle Alpha’s upcoming webinar on June 9th, we explore how agentic AI workflows are transforming data intelligence, market analysis, and investment decision-making. The session will examine how multi-agent AI systems move beyond traditional search and chatbot interfaces to continuously monitor, interpret, and contextualize vast amounts of unstructured data. Click here to register.