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FSI Technology for software vendors and FinTechs

Build faster. Test smarter.
Comply easier.

Build faster. Test smarter.
Comply easier.

Build faster. Test smarter.
Comply easier.

AI capabilities for FSI software vendors, FinTechs and system integrators: synthetic test data instead of real customer data, AI-assisted testing, a knowledge base for compliance content and data quality, GDPR-compliant by design.

AI capabilities for FSI software vendors, FinTechs and system integrators: synthetic test data instead of real customer data, AI-assisted testing, a knowledge base for compliance content and data quality, GDPR-compliant by design.

✓

Synthetic test data instead of real customer data

✓

AI-assisted tests and compliance docs per release

✓

Hosted in Frankfurt, PII filter, SOC 2 Type I certified

Two people talking at a bright table with a view of the city.
ACCELERAID

Test data request

Example

Request

“50,000 card accounts with edge cases for release 4.2.”

Schema from reference data, no real data.

Result

Synthetic dataset ready, distributions checked.

Export via API into the CI/CD pipeline, logged.

○

Illustrative concept

Your rules

The Real Problems

Four things keeping your CTO awake at night.

Every FSI software vendor and FinTech faces them. Most accept them as the cost of doing business. They are not.

Problem 01

Real customer data in your test environment. Every single night.

Real customer data in your test environment. Every single night.

Your QA engineers need realistic financial data to test. So someone — always — ends up with production data in the test database. One audit finding away from a GDPR incident report.

Problem 02

Compliance documentation that takes 3 weeks and a lawyer.

Compliance documentation that takes 3 weeks and a lawyer.

Every release needs MaRisk documentation, GDPR processing records, audit logs. Your developers write code on Monday and compliance docs on Friday. Neither job gets done properly.

Problem 03

Your best compliance expert just resigned. She took 8 years of knowledge with her.

Your best compliance expert just resigned. She took 8 years of knowledge with her.

Regulatory knowledge lives in people’s heads, email threads and outdated wikis. When the expert leaves, the knowledge leaves. The next BaFin audit finds gaps.

Problem 04

QA takes longer than development. Every sprint.

QA takes longer than development. Every sprint.

Manual test cycles, edge cases nobody thought of, regression testing done by hand. Your releases slip. Your clients are unhappy. Your team is frustrated.

Four Capabilities

One platform. Four AI modules for FSI tech providers.

Click any module to jump directly to the full detail section below.

01

Synthetic Test Data

02

SDLC Automation

03

Knowledge Base & RAG

04

Data Quality

Module 01

Synthetic Test Data

Generate statistically accurate synthetic financial datasets for QA, stress testing and CI/CD pipelines — without ever touching real customer data. GDPR risk in testing minimised.

What It Is

Realistic data. Without real customer data.

FSI software providers need to test with realistic financial data — but using real customer data creates GDPR exposure, security risk and approval bottlenecks. ACCELERAID generates synthetic datasets that mirror statistical properties of real data: transaction patterns, account structures, card spending behaviour, portfolio distributions.

No real customer data ever enters test environments

Statistically faithful — preserves distributions, correlations and edge cases

Edge cases and stress scenarios generated on demand

CI/CD integration via REST API or batch export

Covers: transactions, cards, accounts, portfolios, master data

In three steps

1

Reference structure

Analyse the schema and statistical properties of your data without storing or transmitting raw data.

2

Generation

Synthetic datasets with the distributions, correlations and edge cases of the original – never real data.

3

Integration

Delivery via REST API or batch export into CI/CD pipelines, test frameworks and QA environments.

Use cases

QA & regression testing

AI-generated test cases and automatic regression in every release, without real customer data in QA environments.

Integration testing

API mocks and sandbox environments with realistic FSI data structures, for faster partner onboarding and parallel development.

Stress testing & edge cases

Generate rare scenarios, unusual transaction patterns and extreme distributions on purpose and load the system before going live.

Regulatory sandboxes

Test compliance logic against realistic synthetic scenarios, for example on MiFID II, BaFin requirements or DORA, without production data.

Built for FSI

Banking-native data structures

IBANs, MCC codes, card transactions and portfolio hierarchies instead of generic tabular data.

Real data stays with you

We only analyse the schema and statistical structure, not the data itself. Raw customer data is neither transferred nor stored.

Traceable generation

Fine-tuned open-source models and proprietary FSI logic. Every dataset is explainable, not a black box.

01

Synthetic Test Data

02

SDLC Automation

03

Knowledge Base & RAG

04

Data Quality

Module 01

Synthetic Test Data

Generate statistically accurate synthetic financial datasets for QA, stress testing and CI/CD pipelines — without ever touching real customer data. GDPR risk in testing minimised.

What It Is

Realistic data. Without real customer data.

FSI software providers need to test with realistic financial data — but using real customer data creates GDPR exposure, security risk and approval bottlenecks. ACCELERAID generates synthetic datasets that mirror statistical properties of real data: transaction patterns, account structures, card spending behaviour, portfolio distributions.

No real customer data ever enters test environments

Statistically faithful — preserves distributions, correlations and edge cases

Edge cases and stress scenarios generated on demand

CI/CD integration via REST API or batch export

Covers: transactions, cards, accounts, portfolios, master data

In three steps

1

Reference structure

Analyse the schema and statistical properties of your data without storing or transmitting raw data.

2

Generation

Synthetic datasets with the distributions, correlations and edge cases of the original – never real data.

3

Integration

Delivery via REST API or batch export into CI/CD pipelines, test frameworks and QA environments.

Use cases

QA & regression testing

AI-generated test cases and automatic regression in every release, without real customer data in QA environments.

Integration testing

API mocks and sandbox environments with realistic FSI data structures, for faster partner onboarding and parallel development.

Stress testing & edge cases

Generate rare scenarios, unusual transaction patterns and extreme distributions on purpose and load the system before going live.

Regulatory sandboxes

Test compliance logic against realistic synthetic scenarios, for example on MiFID II, BaFin requirements or DORA, without production data.

Built for FSI

Banking-native data structures

IBANs, MCC codes, card transactions and portfolio hierarchies instead of generic tabular data.

Real data stays with you

We only analyse the schema and statistical structure, not the data itself. Raw customer data is neither transferred nor stored.

Traceable generation

Fine-tuned open-source models and proprietary FSI logic. Every dataset is explainable, not a black box.

Synthetic Test Data

SDLC Automation

Knowledge Base & RAG

Data Quality

Module 01

Synthetic Test Data

Generate statistically accurate synthetic financial datasets for QA, stress testing and CI/CD pipelines — without ever touching real customer data. GDPR risk in testing minimised.

What It Is

Realistic data. Without real customer data.

FSI software providers need to test with realistic financial data — but using real customer data creates GDPR exposure, security risk and approval bottlenecks. ACCELERAID generates synthetic datasets that mirror statistical properties of real data: transaction patterns, account structures, card spending behaviour, portfolio distributions.

No real customer data ever enters test environments

Statistically faithful — preserves distributions, correlations and edge cases

Edge cases and stress scenarios generated on demand

CI/CD integration via REST API or batch export

Covers: transactions, cards, accounts, portfolios, master data

In three steps

1

Reference structure

Analyse the schema and statistical properties of your data without storing or transmitting raw data.

2

Generation

Synthetic datasets with the distributions, correlations and edge cases of the original – never real data.

3

Integration

Delivery via REST API or batch export into CI/CD pipelines, test frameworks and QA environments.

Use cases

QA & regression testing

AI-generated test cases and automatic regression in every release, without real customer data in QA environments.

Integration testing

API mocks and sandbox environments with realistic FSI data structures, for faster partner onboarding and parallel development.

Stress testing & edge cases

Generate rare scenarios, unusual transaction patterns and extreme distributions on purpose and load the system before going live.

Regulatory sandboxes

Test compliance logic against realistic synthetic scenarios, for example on MiFID II, BaFin requirements or DORA, without production data.

Built for FSI

Banking-native data structures

IBANs, MCC codes, card transactions and portfolio hierarchies instead of generic tabular data.

Real data stays with you

We only analyse the schema and statistical structure, not the data itself. Raw customer data is neither transferred nor stored.

Traceable generation

Fine-tuned open-source models and proprietary FSI logic. Every dataset is explainable, not a black box.

AI Infrastructure

Best-of-Breed AI — Not a Locked Ecosystem

Best-of-Breed AI — Not a Locked Ecosystem

No own foundation model — by design. OpenAI (via Azure, Germany region) and Google Gemini (Google Cloud, Frankfurt) are standard; on-premise, fine-tuned open-source models or Anthropic on request.

On-premise (on request)

Open source + fine-tuning (on request)

RAG + MCP

OpenAI / Azure (Germany)

Google Gemini

PII filtering before the model call

PII filtering before the model call

Names, IBANs, account numbers and personal details are stripped before any content reaches the LLM. Customer data stays protected across all four modules.

MCP-compatible connectors

MCP-compatible connectors

Connect directly to your existing document management systems, SharePoint, Confluence or proprietary repositories via the Model Context Protocol.

Audit-proof logging

Audit-proof logging

Every query, retrieved passage and generated answer is logged with timestamps. Exportable for internal and external audits.

GDPR by Design

“GDPR-compliant by design” is not a marketing claim. It is an architectural decision.

“GDPR-compliant by design” is not a marketing claim. It is an architectural decision.

The ACCELERAID platform was built from the ground up for regulated environments. PII filtering happens before any data reaches an AI model. Consent management is core infrastructure, not a checkbox. German hosting is the default, not an option.

If your GDPR officer wants to understand the architecture before signing off, we will walk through it with them personally.

SOC 2 Type I certified

Independent audit of security, availability and confidentiality controls.

Platform in an ISO 27001-certified data centre in Frankfurt

LLM hosting on Azure Germany or Google Cloud Frankfurt; platform in an ISO 27001-certified data centre in Frankfurt.

Built for DORA-regulated institutions, aligned with MaRisk

Architecture aligned with MaRisk and BSI IT-Grundschutz, built for DORA-regulated institutions.

+15%

Avg. conversion uplift

250+

Enterprise projects

3.5bn

Transactions analysed

6–9 mo

Avg. time to ROI

Ready to build faster, test smarter and comply easier?

Ready to build faster, test smarter and comply easier?

Tell us which module fits your challenge first — synthetic data, SDLC automation, knowledge base or data quality. We’ll show you exactly what’s possible.