Expertise

Artificial Intelligence

Production-grade AI for regulated financial institutions — fraud detection, credit risk scoring, conversational banking, and compliance automation. Built for the constraints supervisors actually care about: explainability, auditability, and data residency.

NVIDIA DGX SPARK AI Supercomputer

AI is no longer a differentiator for banks — it is table stakes. The real differentiator is whether your AI works inside the regulatory perimeter: explainable to supervisors, auditable end-to-end, and resilient to the data, latency, and sovereignty constraints that come with operating in regulated finance. We design, build, and deploy AI systems that meet those constraints from day one, not as an afterthought once compliance pushes back.

What we build

Fraud detection & AML

Real-time transaction scoring, behavioural analytics, and anomaly detection tuned for card, A2A, and cross-border flows. Our models combine supervised classifiers with unsupervised drift detection, so you catch novel patterns without retraining the entire pipeline. We integrate with your case management and SAR workflows and produce model documentation that maps cleanly to FCA, EBA, and FATF expectations.

Credit risk scoring

Machine learning credit models that improve discrimination over traditional scorecards while remaining explainable to model risk teams and regulators. We use SHAP-based feature attribution, build challenger models for ongoing validation, and document everything under model risk management standards. Suitable for retail lending, SME credit, and behavioural reassessment of existing books.

Conversational AI & customer service

LLM-powered assistants for retail and business banking — handling balance enquiries, transaction disputes, product information, and onboarding triage. We build with strict tool-calling boundaries, PII redaction, hallucination guardrails, and full conversation logging. Deployable fully on-premise on your own GPU infrastructure using open-weight models, or via approved cloud LLM endpoints, depending on your data residency posture.

Predictive analytics

Customer lifetime value, churn prediction, next-best-action, deposit attrition, and cash flow forecasting. We focus on models that drive a specific business action — not dashboards that look good in a board pack. Each engagement includes a measurement plan so the value of the model is provable in revenue, retention, or cost terms within a defined window.

NLP for compliance & regulatory reporting

Automated extraction of obligations from regulatory text (CRR3, DORA, MiCA, PSD3), classification of legal and contractual documents, and structured summarisation for compliance teams. We also build internal regulatory horizon-scanning systems that monitor changes across jurisdictions and route them to the right owners, replacing manual review of supervisory feeds.

Process automation

Loan origination, KYC refresh, onboarding triage, exception handling in payment operations, and reconciliation. We combine document understanding with ML-driven classification so the automation degrades gracefully when documents drift from template — handling the long tail of real-world cases, not just the demo path.

Why banks work with us on AI

Built inside the regulatory perimeter

Every model we deliver ships with documentation aligned to model risk management standards — feature attribution, challenger models, monitoring thresholds, and rollback procedures. Your second line and supervisors get what they need without a retrofit project.

Sovereign and on-premise options

For institutions that cannot send customer data to public cloud LLM endpoints, we deploy on local infrastructure using open-weight models you control end-to-end. No data leaves your perimeter.

Production-grade, not proof-of-concept

We measure success in models running in production with documented owners, monitoring, and a retraining cadence. Engagements include the handover, not just the build. If a model is not still working a year later, we did not finish the job.

Integrated with the systems you already run

Core banking, card processors, AML platforms, data warehouses. Our team has hands-on experience with the integration patterns that matter — message bus contracts, latency budgets, and failover behaviour — not just the model layer.

Vendor-neutral across the AI stack

We work across the open-weight model ecosystem (Llama, Mistral, Qwen) and the major hosted LLM providers, with no partner programme dictating recommendations. The right model for the workload, regulatory geography, and cost envelope wins — and the recommendation may differ across use cases in the same institution.

Knowledge transfer to your data science team

An AI system that only its consultants understand is a model risk liability. We design engagements so your internal teams can extend, retrain, and govern the models after we leave — through documented patterns, paired implementation, and explicit handover workstreams.

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