AI development
for financial services.
Financial services was applying machine learning long before it was called AI, which means the bar is different: your model will meet a model risk management function, and it will need documentation, challenger comparisons, and an explanation for every adverse decision. We build systems that survive that review.
Discuss your projectHigh-value use cases.
Fraud and AML
Supervised models for known patterns alongside anomaly detection for novel behaviour. Adversaries adapt deliberately, so this is one of the few domains where frequent retraining genuinely earns its cost.
Credit decisioning
Risk models where explainability is a legal requirement, not a preference. Adverse action notices mean every decline needs a defensible reason.
Document intelligence
Extraction from KYC packs, loan files, and trade confirmations. Unglamorous, immediately measurable, and usually the fastest payback available.
Client servicing
Retrieval-grounded assistants over product and policy documents, using RAG so answers are citable and correctable without retraining.
Surveillance and conduct
Flagging communications for review under market abuse rules. A ranking-and-triage problem, not a binary judgement.
Churn and next-best-action
Propensity models driving retention and cross-sell, with the commercial rules kept in an auditable layer outside the model.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in financial services.
- Model risk management — expect independent validation, documented assumptions, and a challenger model
- Adverse action explainability — declines require specific reasons a customer can act on
- Fair lending exposure — proxy discrimination through correlated features is a live legal risk, not a theoretical one
- Adversarial drift — fraud patterns change deliberately — see model drift
- Extreme class imbalance — base rates under 1% make accuracy meaningless and alert volume the real constraint
- Full auditability — you must be able to reproduce any historical decision from versioned code, data, and model
AI in financial services.
Will regulators accept a machine learning credit model?
Generally yes, provided you can explain individual decisions, evidence that the model does not discriminate against protected classes including through proxies, and document development and validation thoroughly. Gradient-boosted trees with SHAP explanations are widely used and accepted. The practical constraint is rarely the algorithm — it is whether your governance documentation, monitoring, and challenger comparison stand up to independent review.
How do we handle the false positive rate in fraud detection?
Treat review capacity as the binding constraint rather than a statistical threshold. With a fraud rate near 0.1%, even an excellent detector produces far more false alarms than true ones, so calibrate alert volume to what your team can actually process, rank by expected loss rather than emitting binary flags, group related alerts, and attach the context reviewers need to triage quickly. Feed their decisions back as labels so the model improves on your actual case mix.
Can we run AI on customer data without sending it to a third party?
Yes. Open-weights models deployed inside your own cloud tenancy or on-premise keep data within your control boundary, which is often the deciding factor for regulated workloads. Expect to trade some capability against the frontier hosted models, and expect to own the operational burden. For many financial services tasks — extraction, classification, retrieval — a well-tuned smaller model in your own environment is entirely sufficient.
Other industries.
Healthcare
Custom AI and ML development for healthcare — clinical decision support, imaging, and documentation, built for HIPAA, audit, and c…
ReadLogistics & Supply Chain
Custom AI and ML for logistics and supply chain — demand forecasting, ETA prediction, routing, and exception management built on m…
ReadManufacturing
Custom AI and ML for manufacturing — visual inspection, predictive maintenance, and yield optimisation built for factory-floor con…
ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in financial services and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]