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Top Synthetic Data Use Cases for the Financial Industry

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Turn Finance Data Challenges into Opportunities

The financial sector faces immense pressure from evolving regulations, increasing cyber risks, and customer expectations for seamless digital experiences. Yet, traditional data management practices hinder innovation and expose organizations to compliance and operational risks.

Syntho’s synthetic data technology empowers financial teams to move faster, safer, and smarter by:

  • Accelerating development and testing cycles with instantly accessible, production-like test data
  • Strengthening AI/ML models for fraud detection, credit scoring, and risk analysis using bias-free synthetic datasets
  • Replacing production data in QA, analytics, and PoCs with secure, privacy-preserving synthetic alternatives
  • Unlocking collaboration with partners, vendors, and regulators through compliant, shareable datasets

95%

Synthetic data generation can lead to up to 95% cost reduction, as data is always available, secure, and compliant

70%

Companies using AI-driven test data generation can reduce test cycle times by 70%

50%

AI-driven test data generation can reduce QA team effort by up to 50%, allowing faster test cycles and fewer defects

40%

Institutions can lower test data management overhead by up to 40%, enabling teams to shift focus from maintenance to innovation

Strategic Use Cases of Synthetic Data in Financial Services

Discover how leading banks, fintechs, and insurers apply synthetic data to accelerate transformation, ensure compliance, and protect sensitive information.

Test Data Management (DTAP Environments)

Develop, test, and deploy across Development, Testing, Acceptance, and Production environments with high-fidelity, non-sensitive datasets.

 

  • Automate testing across development, QA, and acceptance

  • Maintain referential integrity across systems

  • Detect 40% more defects with consistent, high-quality data

Process Innovation

Modernize systems, migrate to cloud, and implement new workflows with safe, representative data.

 

  • Removes delays caused by data access restrictions

  • Enable safe testing of cloud and migration workflows

  • Enables rapid validation of digital transformation initiatives
AI Innovation & Predictive Modeling

Enhance model performance, fairness, and compliance by training on diverse and realistic synthetic datasets.

 

  • Simulates rare financial events such as fraud, AML triggers, and economic stress

  • Enables continuous AI/ML improvements without violating data privacy
  • Meets regulatory requirements for explainability and audit trails

Secure & Compliant Data Sharing

Streamline internal collaboration and external partnerships without exposing sensitive customer information.

 

  • Generates privacy-safe datasets for regulators, fintech partnerships, and research institutions

  • Accelerates access while preserving analytical utility

  • Enables cross-border data exchange aligned with global compliance standards

Synthetic Demo Data & Training Environments

Deliver immersive product demos, internal training, and academic programs using real-like, risk-free datasets.

 

  • Create dynamic demo data aligned with real-world use cases

  • Support compliance, onboarding, and education without regulatory hurdles

  • Eliminate delays from manual demo data prep

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