Synthetic Data in Finance

• Synthetic data in finance is defined as artificially generated datasets designed to replicate real financial records while ensuring privacy protection.
• Multiple methodologies—including rule-based simulations, statistical copulas, and machine learning techniques like GANs—enable effective applications in fraud detection, credit scoring, and stress testing.
• Robust evaluation protocols measure statistical fidelity, privacy leakage, and downstream utility, ensuring compliance with regulatory standards and improving model performance.

Synthetic data in finance refers to artificially generated datasets that statistically resemble real financial records while deliberately containing no actual personal or proprietary entries. This paradigm has gained traction as a core privacy-enhancing technology, enabling robust AI model development in environments constrained by privacy laws, data scarcity, or cross-institutional non-disclosure requirements. Synthetic data underpins workflows in fraud detection, credit risk scoring, stress testing, model validation, and collaborative analytics—permitting computational research and operational deployment without direct access to sensitive customer or market data (Godbole, 16 Mar 2025, Efimov et al., 2020, Meldrum et al., 30 Oct 2025).

1. Regulatory Context and Motivations

The finance sector is shaped by strict privacy and usage regulations, such as the EU GDPR, the California CCPA, and industry-specific guidelines (e.g., US SR 11-7 for model risk). These legal frameworks severely restrict the sharing, processing, and external transfer of consumer financial data. Simultaneously, the sector faces data limitations:

• Highly imbalanced datasets, for example, credit card fraud rates below 0.2% in transaction streams.
• Scarcity of observable rare events (e.g., flash crashes, market manipulations).
• Inability to freely share real datasets across business units, partners, or regulatory bodies.

Synthetic data generation offers compliance by producing datasets that mimic, but do not reproduce, the real data distribution, removing personally identifiable information (PII) and thus decoupling utility from disclosure risk (Godbole, 16 Mar 2025, Meldrum et al., 30 Oct 2025). This unlocks a spectrum of data-centric AI initiatives while addressing both privacy preservation and data augmentation.

2. Synthetic Data Generation Methodologies

A taxonomy of synthetic data generation methods in finance includes (Godbole, 16 Mar 2025, Efimov et al., 2020, Meldrum et al., 30 Oct 2025, Potluru et al., 2023):

A. Rule-based Simulation

• Utilizes curated business and regulatory logic, generating synthetic ledgers or credit profiles by enforcing deterministic constraints (e.g., double-entry balance rules).
• Guarantees business validity, but cannot reproduce high-dimensional relationships observed in empirical data.

B. Statistical and Copula Approaches

• Fits univariate marginals (parametric or nonparametric) and models joint dependencies using copulas (e.g., Gaussian, t, or vine copulas).
• Monte Carlo simulation samples scenarios for risk (VaR) and stress-test purposes.
• This class excels for low-dimensional, tabular data with clear business semantics and moderate complexity.

C. Machine-Learning-Based Generators

• GANs: Conditional GANs (CGANs) allow explicit minority/rare-class control; loss:

$$\min_G \max_D V(D, G) = \mathbb{E}_{x\sim p_{\mathrm{real}}}[\log D(x)] + \mathbb{E}_{z\sim p_z}[\log(1 - D(G(z)))]$$

• VAEs: Encode $x\rightarrow z\sim q_{\phi}(z|x)$, then decode $z\rightarrow \hat{x}\sim p_{\theta}(x|z)$; optimized via ELBO.
• Advances such as Conditional DRAGAN integrate conditionality and regularization to enhance stability and performance on mixed-type (categorical and continuous) tabular data (Efimov et al., 2020).
• Diffusion models and normalizing flows are emerging for time-series synthesis (Karst et al., 19 Dec 2024, Rožanec et al., 1 Oct 2025).
• Domain-specific: Hawkes processes for event-series (trade arrivals), TimeGAN or Fiaingen for high-frequency return sequences (Potluru et al., 2023, Rožanec et al., 1 Oct 2025).

D. Hybrid Techniques

• Combine statistical and ML-based marginals or copulas with rule-based or business constraints, enabling, e.g., regulatory limit enforcement post-generation (Godbole, 16 Mar 2025).

3. Evaluation Protocols and Quality Metrics

Rigorous evaluation of synthetic financial data spans statistical fidelity, privacy leakage, and downstream utility (Godbole, 16 Mar 2025, Visani et al., 2022, Caceres et al., 29 Oct 2024, Meldrum et al., 30 Oct 2025):

Metric Type	Example Metrics / Strategies
Statistical Fidelity	KL-divergence: $D_{KL}(P‖Q)=\sum_x P(x)\log\frac{P(x)}{Q(x)}$;
	Maximum Mean Discrepancy (MMD); 1-Wasserstein; univariate/bivariate KS statistic;
	Mahalanobis distance in PCA space; t-SNE embedding overlap
Privacy/Disclosure	Differential Privacy: $(\epsilon, \delta)$-DP constraints, per-record sensitivity;
	Membership Inference Advantage (MIA); nearest-neighbor distance ratio;
	Fraction of synthetic records reconstructable from real data
Downstream Utility	Train-synthetic-test-real (TSTR) performance: AUC-ROC, recall for rare event, VaR/ES;
	Comparison of model performance when trained on synthetic vs. real data
Business Consistency	Adherence to balance/ledger rules; absence of impossible record combinations;
	Stress scenario domain alignment; domain-expert qualitative review

Best-practice frameworks, such as DAISYnt, systematize these tests across distributional, utility, and privacy axes (Visani et al., 2022). For regulatory scenarios, post-processing (e.g., de-binning, inverse transforms) is included in the end-to-end utility audit (Caceres et al., 29 Oct 2024).

4. Applications in Financial Practice

Synthetic data underpins a range of production and research applications:

Application	Purpose	Generator Type	Noted Results / Case Studies
Fraud Detection & AML	Train on rare-event synthetic transactions, expand coverage of novel fraud or laundering patterns	CGAN, rule-based, hybrid	RF models trained only on synthetic: AUC-ROC ≈ 0.93 (Godbole, 16 Mar 2025); GAN-augmented LSTM improved recall by 2 pts (Jiang et al., 4 Dec 2024)
Credit Scoring	Fill gaps in applicant attribute space, enable privacy-preserving behavioral scoring	VAE, Copula, CGAN	Models trained on synthetic had ~3% AUC decrease (Muñoz-Cancino et al., 2022); SC-GOAT mixture closes >50% of real-synthetic gap (Nakamura-Sakai et al., 2023)
Model Risk & Stress Testing	Synthetic scenario generation under tail or regulatory-defined shocks	Copula, GAN, BiGAN	Marginals and stress metrics preserved below 3% error (Caceres et al., 29 Oct 2024); deep factor/residual pipelines enforce stylized facts (Cetingoz et al., 7 Jan 2025)
Financial Supervision	Augment minority class, improve detection of market misconduct, systemic risk	CGAN, LSTM-GAN	GAN-augmented datasets improved F1 by 1–2 points over SMOTE (Jiang et al., 4 Dec 2024)
Regulatory Data Sharing	Privacy-compliant microdata for public release and research; central bank statistical products	MST/AIM marginal-based	Aggregated index error <0.03, outperformed GAN variants (Caceres et al., 29 Oct 2024)
Trading & Backtesting	Generate high-fidelity synthetic price series and limit-order books to stress trading algos	Fiaingen, TimeGAN, Diffusion	Fiaingen: synthetic-only ROC-AUC within 10 pp of real-only; runtime 1000x faster than deep nets (Rožanec et al., 1 Oct 2025)
Model Drift Stability	Improve resilience to distribution shifts, especially in volatile emerging markets	zGAN + EVT outliers	Optimal (~5–10%) synthetic outlier injection maximizes stabilization score (Varshavskiy et al., 10 Oct 2025)

5. Challenges, Limitations, and Ethical Risks

Synthetic data for financial systems is characterized by inherent trade-offs (Godbole, 16 Mar 2025, Balch et al., 20 Mar 2024, Caceres et al., 29 Oct 2024, Cetingoz et al., 7 Jan 2025):

• Fidelity Loss: Unless high-order dependencies (especially tail dependencies) are preserved, models trained on synthetic data risk misestimating portfolio risk (VaR, ES), credit losses, or rare event probabilities. For tabular fraud data, the recall for minority class events typically drops when using only synthetic data (e.g., 76% vs. 22% when trained on real vs. CGAN-synthesized (Godbole, 16 Mar 2025)).
• Bias Amplification: Synthetic generators can reinforce spurious correlations (e.g., demographic bias in loan approvals) unless bias audits and post-hoc correction are enforced (Godbole, 16 Mar 2025).
• Privacy Leakage: Without formal DP or singling-out/linkage tests, overfitting generators can leak individual attributes. Best practice incorporates per-batch DP noise or adversarial audit gates (Caceres et al., 29 Oct 2024, Visani et al., 2022).
• Validation Bottlenecks: Regulatory stakeholders demand back-testing on real data and traceability from synthetic to real attribute space.
• Interpretability: Attribution of model decisions is hindered by additional abstraction between synthetic features and their real-world semantics (Godbole, 16 Mar 2025).

6. Best Practices and Recommendations

Technical and governance recommendations for financial synthetic data pipelines include (Godbole, 16 Mar 2025, Visani et al., 2022, Balch et al., 20 Mar 2024, Caceres et al., 29 Oct 2024):

• Data Preprocessing: Analyze and correct for class imbalances, outlier distributions, and regulatory constraints before synthesis.
• Method Selection: Match generator type to data character (statistical for low-d and continuous, CGAN/TVAE for high-d mixed, hybrid when rules must be enforced).
• Privacy Safeguards: Integrate DP at model or post-hoc level; ensure $k$-anonymity, $\ell$-diversity, and $t$-closeness for quasi-identifiers.
• Evaluation Rigor: Use both statistical (KL, MMD), adversarial (distinguishability, linkage), and downstream utility (AUC, VaR error) metrics. Domain-specific metrics (VaR/ES error under stress) are critical for risk and trading.
• Documentation & Audit: Maintain logs of generator architecture, hyperparameters, privacy budget, evaluation outcomes, and data-processing pipelines for compliance review.
• Continuous Monitoring: Retrain/validate generators as real-world data evolves to counteract model and data drift.
• Ethical Oversight: Cross-functional review with compliance and ethics experts to mitigate bias, ensure fairness, and monitor secondary risks.

7. Trends and Open Directions

Recent systematic reviews highlight a dominance of GAN-based approaches, but also significant research gaps in privacy benchmarking, hybrid modeling, and scenario-centric evaluation (Meldrum et al., 30 Oct 2025, Potluru et al., 2023). Emerging topics include:

• Meta-model Optimization: Blending generators and tuning via Bayesian or meta-learning to optimize for downstream task performance (e.g., SC-GOAT (Nakamura-Sakai et al., 2023)).
• Scenario-Conditioned Synthesis: Direct macro or exogenous conditioning in scenario generation pipelines (e.g., portfolio stress, ESG shocks) (Rizzato et al., 2022).
• Lightweight, High-Fidelity Time Series Generation: Graph-based methods (e.g., Fiaingen) rival deep neural models in fidelity and runtime for financial time series (Rožanec et al., 1 Oct 2025).
• Regurgitative Training for Identifiability: Model retrain-on-synthetic-data as a robust application-level validation test (Cetingoz et al., 7 Jan 2025).
• Regulatory/Industry Benchmarks: Need for sector-wide adoption of privacy and utility auditing suites (e.g., DAISYnt, full DP guarantee and utility reporting) (Visani et al., 2022, Potluru et al., 2023).
• Extending Theory for Heavy-Tailed and Rare-Event Synthesis: Current neural approaches may underperform for the extremal regimes critical in financial risk.

Synthetic data, when engineered and validated with strict domain, regulatory, and technical rigor, is positioned as a key enabler for privacy-preserving AI, safe cross-organization analytics, and robust stress testing in modern financial institutions (Godbole, 16 Mar 2025, Meldrum et al., 30 Oct 2025).