Underwriters are reading PDFs by hand
Bank statements, KYC packs and claim documents arrive as scans, and someone keys them in.
Home / Industries / Financial Services
Financial services
AI you can explain to a regulator, line by line.
Document-heavy workflows, model governance and infrastructure for lenders, insurers, brokers and fintechs. Accuracy matters; being able to prove it matters more.
The three things we hear in the first meeting.
If none of these sound like you, we are probably not the right call yet — and we will say so.
No one can explain the model's decision
An accurate score you cannot justify is a compliance liability, not an asset.
Data residency is not negotiable
Regulated customer data has to stay in-country, in systems you can audit.
Explainability is an architecture decision, not a feature.
If the system has to justify itself later, it has to record the right things now. We design for the audit before we design for the accuracy.
-
Every decision traceable.
Inputs, retrieved evidence, model version and output, logged and queryable for the retention period you need.
-
Data stays in-country.
Deployed in your own cloud account or a dedicated in-region Stellar Cascade tenancy.
-
Humans stay in the loop where it counts.
Automation for extraction and triage; human decision where the regulation requires one.
-
Model changes are controlled.
Versioned models, regression evaluation and an approval path before anything reaches production.
Who we build for
Different teams, different first project. The platform underneath is the same.
Lenders and NBFCs
Document extraction, income verification and early-warning signals on the book.
Insurers
Claims intake, fraud triage and policy-document search for contact-centre teams.
Capital markets
Research summarisation, filings search and structured extraction from disclosures.
Fintech products
AI features built to survive both a bank partner's audit and an RBI-facing review.
What we actually deliver
Four things, in this order. Each one is useful on its own.
AI Discovery with risk in the room
Two weeks with operations, risk and technology to pick the defensible use cases.
Document intelligence pipeline
Extraction from scans and PDFs with confidence scores and human review for low-confidence cases.
Model governance layer
Versioning, evaluation, approval workflow and the audit log behind it.
In-country infrastructure
Dedicated compute with residency guarantees, backups and disaster recovery.
How a financial services engagement runs
No surprises about sequence, and no invoice before there is something to look at.
-
01
Discovery with risk present
Risk and compliance sit in the first workshop, not the final review. It saves months.
-
02
Control design
What is logged, who approves changes, how a decision is reconstructed twelve months later.
-
03
Pilot on historical data
We run against closed cases first, so accuracy is measured before anything touches a live customer.
-
04
Controlled rollout
Shadow mode, then partial automation, then full — each gated on measured performance.
What good looks like
- 100%
- Decisions with a reconstructable trail
- In-country
- Data residency, verifiable
- Shadow-first
- No live decisions before measurement
- Versioned
- Every model change approved and logged
The Financial Services AI readiness checklist
The questions we work through before recommending anything — data readiness, hosting constraints, review process and the running cost at year two. Use it with any vendor.
Financial services questions we get asked first
Short answers. Longer ones are a conversation.
Can you prove why the model returned a particular result?
For retrieval-based systems, yes — the evidence the model saw is logged alongside the answer, so a reviewer can reconstruct the decision. For scoring models, we favour approaches that carry feature-level attribution. Where a use case genuinely cannot be explained, we say so and recommend keeping a human decision-maker.
Does data leave India?
Not unless you choose that. Deployments run in your own cloud account in an Indian region, on your own hardware, or in a dedicated in-country Stellar Cascade tenancy.
How accurate is document extraction on poor-quality scans?
It varies by document type and scan quality, which is why we benchmark on your actual documents during discovery rather than quoting a headline number. Low-confidence extractions are routed to a human instead of being guessed.
Will this pass an internal audit?
We build the logging, versioning and approval controls that audits ask about, and hand over the documentation. Your audit team makes the call, but they should not find gaps in the trail.
Can you integrate with our core banking or policy system?
Yes. Integration is usually via API, message queue or scheduled file exchange depending on what the vendor supports. We settle the approach during discovery.
Bring us the workflow your team dreads.
Document-heavy, rules-heavy, audit-heavy. Those are the ones worth automating properly.
- A reply within one working day, from an engineer rather than an account manager.
- An honest read on whether this is worth doing now, or in a year.
- No marketing list. Your details reach the solutions team and stop there.