Industries

Enterprise AI for Financial Services & Capital Markets

AI in financial services is applied primarily to document-heavy and evidence-heavy processes — underwriting, prospectus and filing review, regulatory change management, and risk assessment — where the work is reading, checking and reconciling rather than calculating.

Financial institutions run on documents and on obligations. A credit file is a stack of them, a prospectus review is a structured read of several hundred pages, a regulatory change is a text that has to be mapped against every internal policy it touches. The volume is high, the deadlines are external and immovable, and the cost of missing something is measured in enforcement action rather than inconvenience.

This is the profile AI addresses best, with one non-negotiable condition: every output has to be traceable. An extracted figure needs to point at the page it came from, an answer needs to cite the paragraph it rests on, and the whole chain needs to survive examination by an auditor or a regulator months later.

That requirement rules out a large class of AI systems that produce plausible output with no provenance, and it is the main reason financial services pilots stall at the point of moving to production.

Where it applies

The strongest use cases are the ones where skilled people currently spend their time reading rather than deciding.

IPO prospectus and offering document review, with cross-referencing against prior filings and disclosure requirements
Credit underwriting and loan file automation — extraction, completeness checking and exception routing
Regulatory change detection, mapping a new rule to the internal policies and procedures it affects
Policy and procedure retrieval that answers with the current version and cites the clause
KYC and onboarding evidence collection, verification and escalation
Merchant and transaction risk scoring supported by document and behavioural evidence
Automated reporting with variance explanation drawn from underlying data

Built for examination

Every system we build for this sector assumes it will be examined. Extracted fields carry provenance to the page and span. Retrieval answers carry citations. Agentic actions carry a complete trace of the evidence considered and the decision made. Model versions, prompts and evaluation results are recorded, so the question of what the system would have said at the time it said it has an answer.

Approval gates are placed wherever a decision has a customer or regulatory consequence. In underwriting that generally means the system assembles, checks and recommends, and a person decides — which is also what makes the deployment defensible to a model risk function.

Where data cannot leave

Much of this work involves material non-public information, customer data under residency obligations, or corpora that contractually cannot be transmitted to a third party. Private LLM deployment inside the institution's own tenancy or data centre removes the question entirely — prompts and documents never cross the boundary — and is frequently the difference between a pilot and a production system.

02Where It Applies

Typical applications in bfsi & capital markets.

IPO prospectus and offering document review automation

Regulatory change detection and internal policy impact mapping

Credit underwriting and loan document processing

KYC, AML and onboarding evidence automation

Contract and obligation extraction across counterparty agreements

Policy and procedure assistants for front-line staff

Merchant fraud detection and risk scoring

Financial reporting with automated variance analysis

03Questions

BFSI & Capital Markets, answered.

The questions that come up on almost every first call in this sector.

How do we satisfy model risk management and audit?

By building for it rather than retrofitting it. That means documented model selection with benchmark evidence, a versioned evaluation suite with recorded results, full provenance on every extracted field and cited answer, logged prompts and model versions, and explicit human approval gates on consequential decisions. These artefacts are deliverables of the engagement, not paperwork produced afterwards — which is usually where retrofitted pilots fail their first review.

Can any of this run without sending data to a third-party model provider?

Yes, and for many institutions it must. Open-weight models deployed inside your own tenancy or data centre keep prompts, documents and outputs entirely within your boundary. We benchmark candidate models on your actual tasks so the trade-off between a private deployment and a commercial endpoint is settled with evidence rather than assumption.

Does the system make lending or risk decisions?

Not in the deployments we recommend. It assembles the file, extracts and checks the fields, flags what is missing or inconsistent, and presents the evidence — a person decides. That division keeps the decision with an accountable human, which is both what regulators expect and where the honest capability boundary currently sits. It is also where the time saving already lives: most underwriting effort is assembly and checking, not judgement.

How do you handle documents that contain material non-public information?

With a private deployment, access controls inherited from the source systems, permission-aware retrieval so users only see answers built from documents they could have opened, and logged access. Information barriers that exist in your organisation are enforced in the retrieval layer rather than assumed away.

04Capabilities