Fintech
On-premise fraud detection, credit decisioning, and AML reasoning.
Financial services require models that are auditable, explainable, and provably air-gapped from third-party infrastructure. Sending transaction data to a frontier API violates most bank data-sovereignty policies. A specialist model trained and served on infrastructure you control satisfies the compliance requirement without sacrificing capability.
Why a specialist model
Three reasons frontier models do not fit.
- Regulatory data sovereignty prohibits API egress
- FCA, PRA, and equivalent regulators require that customer financial data stays within defined geographic and organisational boundaries. A frontier API call is a data export. A specialist model deployed on your own infrastructure is not.
- Credit and fraud decisions must be explainable
- Automated credit decisions and fraud flags must be explainable to regulators and challengeable by customers under GDPR Article 22. A specialist model produces evidence bundles at every promotion, giving compliance teams an auditable record of what the model was trained on and why it was approved.
- Frontier cost per transaction is unviable at volume
- Running every card authorisation or loan application through a frontier API at commercial rates is not economically viable for high-volume operations. A specialist on owned hardware processes transactions at a fixed infrastructure cost independent of volume.
Use cases
Concrete workflows, not a category claim.
Each use case below maps to a real workflow a design-partner team would bring to Modelsmith. The specialist model is trained on your data, evaluated against your rubric, and promoted through your governance gate.
Real-time fraud scoring
Train a specialist on your labelled fraud cases to score transactions at authorisation time. The model learns patterns specific to your customer base and product mix, not a generalised fraud taxonomy built on someone else's data.
Credit decisioning
Fine-tune a specialist on your historical application and outcome data to produce a credit recommendation with a structured rationale. The rationale supports the GDPR right-to-explanation requirement for automated decisions.
Transaction-pattern reasoning
Use a specialist to identify unusual transaction sequences and generate a narrative description of the pattern for analyst review. Reduces the time an analyst spends reconstructing context from raw transaction logs.
AML pattern detection
Train a specialist on your AML-labelled transaction graph samples to flag structuring, layering, and placement patterns. The model surfaces the specific transactions that match the pattern and ranks them by confidence for SAR review.
Get started
Bring a fintech workflow to the design-partner cohort.
Apply to the design-partner programme with your workflow in mind. We will scope the Synthetic POC together, run a complete specialisation cycle on synthetic domain scenarios, and hand you a validated model with a full evidence bundle before any licence commitment.