Fraud detection, automated KYC summaries, compliance RAG, with strict data sovereignty and audit logging.
The financial sector operates in two opposing force fields: extreme regulation (GDPR, MiFID, DORA, NIS2) and extreme competition on the digital experience front. AI only works here if data governance and auditability are primary concerns, not afterthoughts.
Our on-prem-focused deployments deliver exactly this: customer data never leaves the environment, every decision is traceable, and regulatory review is seamless.
On-prem and air-gapped options, aligned with industry-specific regulations.
Every deployment tied to a concrete business metric, not an AI project, a business project.
Every decision traceable, every answer cited, every access logged.
Industry-specific use cases, terminology, integration patterns, not generic AI.
These are the most commonly deployed use cases in this vertical. Each is calibrated to the industry's actual needs, with measurable ROI.
Transaction monitoring, anomaly detection, suspicious pattern flagging, with explainable AI so human review is fast and auditable.
Speed up customer onboarding: document extraction, risk profile, sanctions screening combined into a decision-supporting format.
Internal regulatory RAG: searchable, citing assistant over MiFID, central bank regulations, AML codes for the compliance team.
In financial services data cannot leave the controlled environment, and every AI-supported decision must be reconstructable afterwards. Local or dedicated deployment with full audit logging is almost always the answer here.
We start where repetitive effort and the cost of error are both high: transaction review, client identification documents, report preparation. Payback is quantifiable here.
Which client data the system may touch and in what controlled environment. This step decides how every AI-supported decision will later be reconstructed.
We measure on closed, real cases where the outcome is already known. That makes the false alarm rate and the number of caught cases quantifiable before any live traffic.
The live system proposes, a person decides. We keep measuring model behaviour because fraud patterns shift, and a threshold that worked yesterday misfires today.
These are the applications that pay back fastest in this sector. Each can be deployed independently.
| Use case | What it does | What it delivers |
|---|---|---|
| Fraud detection | Transaction patterns and anomalies | Fewer false positives, faster triage |
| KYC summarisation | Extracting from client documents | Shorter turnaround, consistent quality |
| Internal regulatory RAG | Regulator circulars, internal codes | Answers backed by citations |
| Client communication | Drafting correspondence and complaints | Faster response, human approval |
| Risk reporting | Consolidating data sources | Automated monthly and quarterly packs |
On closed historical cases where the outcome is already known. That makes the false alarm rate measurable without live traffic, and the rollout decision rests on numbers. Risk during the measurement phase is zero.
For anomaly detection and document summarisation current models perform reliably. They do not make risk decisions: they propose, and a person approves. We keep measuring model behaviour because fraud patterns shift.
Not with a local or dedicated deployment. In the systems we build, data does not leave the infrastructure, and the model accesses data with the user's privileges rather than an omnipotent service account. The latter is the most common design mistake: without it, anyone reaching the assistant indirectly reaches everything that account can read.
Through complete logging: who asked what and when, which sources informed the answer, which tools were called with what parameters. RAG-based solutions attach source citations to the answer, so the basis is traceable. Without this a wrong answer cannot be traced, nor corrected.