Machine Learning Development built for Fintech companies
What is Machine Learning Development?
We build ML systems engineered to stay accurate after deployment, not just in the notebook. For Fintech companies, this means navigating PCI DSS, PSD2 while shipping fast.
Who needs Machine Learning Development for Fintech?
- Meeting evolving compliance requirements across markets
- Integrating with legacy core banking systems
- Building real-time fraud detection at scale
- Models performing well in testing, poorly in production
- No retraining pipeline as data drifts over time
Best Machine Learning Development company
Software that moves and manages money must be fast, auditable and bulletproof under regulatory scrutiny.
We specialize in Machine Learning Development for Fintech, with a growing track record across industries that move fast.
Start your Machine Learning Development project with a team that knows Fintech
Book a free 30-minute discovery callCommon Machine Learning Development mistakes we see across Fintech teams
- Meeting evolving compliance requirements across markets
- Integrating with legacy core banking systems
- Building real-time fraud detection at scale
- Maintaining uptime during peak transaction volume
- Balancing UX simplicity with strict KYC/AML flows
How GarudLabs approaches Machine Learning Development for Fintech companies
ML model architecture and training pipeline
Feature engineering and data pipeline
Model deployment and serving infrastructure
Drift detection and retraining automation
Experiment tracking and model versioning
Core Technology Stack
PythonPyTorchscikit-learnMLflowKubeflowAWS SageMaker
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $170,000
Machine Learning Development engagements in this combination typically need to account for:
- PCI DSS
- PSD2
- SOX
- GDPR
- AML/KYC regulations
Our Approach
For Fintech platforms, our focus is meeting evolving compliance requirements across markets without sacrificing delivery speed — architecture and code review practices are adjusted from day one for PCI DSS and PSD2.
See our case studiesGet a Machine Learning Development proposal tailored to your Fintech goals
Get a free project cost estimateFrequently Asked Questions
Most Machine Learning Development engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Machine Learning Development projects with us typically range from $25,000 - $170,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For machine learning development, we typically work with Python, PyTorch, scikit-learn, MLflow, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical machine learning development engagement includes ml model architecture and training pipeline, along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for machine learning development because of models performing well in testing, poorly in production, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple fintech projects addressing challenges like meeting evolving compliance requirements across markets. We helped a digital lending platform cut loan approval time from days to minutes while staying fully audit-ready.
For fintech clients, we pay close attention to PCI DSS, PSD2, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for fintech clients include meeting evolving compliance requirements across markets and integrating with legacy core banking systems. We tailor our discovery process to surface the specific version of these problems your business faces.