AI for finance and insurance
AI that answers from your policies, contracts and procedures — with the source.
In finance and insurance — industries under tight regulatory oversight — a guessing model is not enough. We build knowledge-based systems — on your documents, with source citations and a full audit trail. On AWS. Your data stays with you.
The problem
Generic AI in a regulated industry is a risk, not a saving.
A public chatbot answers from what it "remembered" during training. It does not know your credit policy, your current fee schedule or last quarter's regulator guidance. A wrong answer about a product, a contract or a compliance procedure costs customer trust — and can cost you the regulator's attention.
In finance an AI answer must have a source. RAG reverses the order: it first retrieves the right passages from your controlled knowledge base, then the model answers — strictly on that basis, with a link to the document. Your staff can see where the information comes from. So can the auditor.
Use cases
Knowledge from your documents — where it is missing today.
Every one of these rests on the same pattern: controlled sources, citation, data with you. No model speculation.
Knowledge base on products and procedures
Staff ask, the system answers from current policies and internal procedures — citing the document and the paragraph. Instead of digging through network folders.
Customer support grounded in sources
A consultant gets a prompt based on the current offer and fee schedule — not a hunch. The answer comes from a document you can show the customer.
Support for analysts and compliance
Searching regulations, policies and contracts in seconds — citing the provision. The analyst gets the passage and the source, not a model output with no justification.
Internal search across policies and contracts
Legal, risk and operations ask about clauses, limits, terms — the system returns the exact passage from the contract or policy. Faster than searching hundreds of files by hand.
Claims document analysis
Claim notifications, policies, correspondence and case files — the system retrieves and classifies the relevant passages, summarises them and cites the source, so an adjuster can assess documentation completeness faster. It supports document analysis, not an automated payout decision — the decision stays with a person.
Risks
The AI Act and data: what you must keep under control in finance and insurance from day one.
The AI Act — some AI systems in finance and insurance are high-risk
AI systems used to assess the creditworthiness or credit score of individuals (other than for fraud detection), and — in insurance — to assess risk and set pricing for life and health insurance, are classed as high-risk under the AI Act (Annex III) — with obligations around documentation, risk management and human oversight. The Digital Omnibus moved the Annex III deadlines to 2 December 2027, but we verify the classification during the audit anyway, before the system reaches production.
GDPR, banking and professional secrecy
Customer data and internal documents cannot travel to public models. We build in your AWS account — you invoke the model, the data stays under your control and in your jurisdiction. Not a statement — architecture.
Auditability and human oversight
A regulator expects the basis of a decision to be explainable. A RAG source citation is not interface decoration, it is part of the audit trail. We design it in from the start, not bolt it on at the end.
Unsure how the AI Act classifies your systems? Start with an EU AI Act compliance audit.
Proof
We built an end-to-end product in production — we bring the same stack to finance.
RAG
versioned knowledge, source citations and controlled refusal
$0.0006
rounded inference-layer cost per AI scan
302,516
drug-interaction records in the production knowledge base
The numbers come from mojApteczka — Semitora's own product in the healthcare domain. They show the architecture and build quality we carry across industries. Case studies from the financial sector — coming soon.
See the full case studyQuestions
Before you ask
No. We build the knowledge base in your own AWS account (Amazon Bedrock Knowledge Bases). Documents, contracts and customer data stay under your control — they do not leave your infrastructure. That matters for GDPR as well as banking and professional secrecy.
ChatGPT answers from general knowledge and does not know your documents. A generic Copilot also guesses if it has no specific integration with your base. RAG connects the model to your controlled knowledge base — answers are specific, cited and limited strictly to what you actually have and have approved.
It depends on what you use AI for. Systems used, for example, to assess creditworthiness can be classed as high-risk — with obligations around technical documentation and human oversight. Knowledge bases and document search usually sit lower in the risk hierarchy, but we always verify the classification during the audit. We do not answer in general terms — we check your systems.
Not in the sense of an automated payout decision. We build systems that analyse and classify claim documents, find the relevant policy clauses and cite the source — to speed up the adjuster's work. The assessment and the decision stay with a person: that keeps the workflow under human oversight and is our design principle.
Priced by scope — it depends on the number and state of the sources, query volume and required integrations. We start with an audit that sets the scope on facts. No hidden stages.
The audit usually takes 2–4 weeks. The production rollout depends on scale and the number of integrations. We can start with a proof of concept (PoC) on a real slice of your documents, before you commit the full budget.
Have policies, procedures and contracts your AI should answer from?
Let's start with an audit: we'll check whether your sources are RAG-ready, where your systems stand on the AI Act and how fast we can ship a working system — without experimenting on your budget.