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Financial Services

Synthetic Data Generation for Financial Services

Synthetic Data Generation for financial services, built around the constraint that defines the sector: every automated decision must be explainable and reproducible months after the fact.

Regulations in scope
5
Systems we integrate
5
Typical first release
6 weeks
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What changes when it is financial services

The most common use is unglamorous and valuable: developers need realistic test data and should not have production customer records on their laptops.

In financial services, every automated decision must be explainable and reproducible months after the fact. That single fact reshapes how synthetic data generation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is regulatory report assembly, usually integrated against SAP and Oracle financials. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.

Multi-model by default, so a provider outage is a routing decision rather than an incident. We hand over with runbooks, tests and a team that knows how it works, not a dependency.

The sector constraints we design around

Defining constraint
every automated decision must be explainable and reproducible months after the fact
Regulations in scope
RBI guidelines · SEBI regulations · DPDP Act 2023 · PMLA and AML rules · IRDAI where insurance applies
Systems of record
core banking · trading and OMS · loan origination · SAP and Oracle financials · regulatory reporting platforms
Where we usually start
credit memo drafting

Synthetic Data Generation workloads in financial services

  • credit memo drafting
  • KYC and onboarding checks
  • regulatory report assembly
  • reconciliation
  • client communication review

What is included

  • Statistical profiling of the source so the synthetic set preserves real relationships
  • Privacy evaluation, including re-identification risk testing
  • Class balancing and rare-event augmentation where models need it
  • Realistic test datasets for non-production environments
  • Validation that models trained on synthetic data actually transfer
  • Documentation for your DPO and auditors

Questions from this sector

Can we use AI in credit decisions?

With explainability, documented model governance and human review on adverse outcomes, yes. RBI expects you to be able to explain any decision that affects a customer.

How do you handle data residency?

Deployment inside Indian regions or on your own infrastructure, which is the usual requirement for regulated financial data.

Is synthetic data private by default?

No. Privacy depends on how it was generated and must be tested. We run re-identification risk assessment rather than asserting anonymity, because regulators ask for evidence.

Can we train production models on it?

Sometimes, particularly for augmentation and class balancing. We validate performance on held-out real data before recommending it for production training.

Does it satisfy DPDP requirements?

Properly generated and tested synthetic data can reduce personal-data exposure meaningfully. We document the method and the risk assessment so your DPO can make that determination.

Synthetic Data Generation in other sectors

BankingInsuranceHealthcare & HospitalsPharmaceuticals & Life SciencesTelecommunicationsSaaS & TechnologyGovernment & Public SectorRetailE-commerceDefence & Aerospace

Other capabilities for financial services

Synthetic Data Generation for financial services, worth a conversation?

Tell us the workload and the regulation it sits under. We will tell you what is realistic.

Talk to an engineerWhatsApp us

Or email bd@dtrasglobal.com · call +91 74118 77878