Industries
AI for fintech that survives the audit.
In fintech, a wrong AI call is not a bad answer, it is an incident. We build AI that works inside that constraint: onboarding cut from days to minutes, documents read with every finding traceable, and humans approving the calls that matter. An AI agency building for fintech teams worldwide since 2019.
Who builds production AI for fintech?
extendfuture, an AI agency, builds production AI for fintech: biometric onboarding, document intelligence, agentic workflows and human-in-the-loop review. Systems ship with least-privilege access, audit logs, PII masking and human approval gates, designed to support DPDP, GDPR and SOC 2 expectations. In one production fintech system, field onboarding went from about two days to about eight minutes, working offline. Engagements start with an audit, sprint or pilot, scoped on a founder-led call.
The problems fintech teams bring us
The pattern is consistent whether the product is lending, payments, wealth or insurance-adjacent. Growth is gated by manual steps: onboarding, verification, document review, reconciliation. And every fix has to survive three audiences at once: the customer who will not wait, the ops team drowning in volume, and the auditor who asks why a decision was made. Generic AI tools fail the third audience. That is usually when we get the call.
- Onboarding that takes days and loses customers, especially in the field or offline
- KYC files, agreements and statements re-keyed by hand between systems
- Decisions that must be explainable to an auditor, not just correct on average
- Customer data that cannot leave your accounts or your jurisdiction
- A pilot that impressed the room and then stalled before production
What we build for fintech
Six services, each with a fixed entry engagement, so the first commitment is small, not open-ended. For fintech the usual entry points are onboarding and verification, document intelligence, and agentic back-office workflows. Everything ships with evals, guardrails, audit logs and human escalation, because in this sector the system around the model is the product.
- Onboarding and verification products, built as AI product development: a Prototype Sprint puts a working system on real data fast
- Document intelligence for KYC files, agreements and reports, via AI process automation: One Workflow Automated fast
- Agents for reconciliation, monitoring and reporting, via agentic AI development: an Agent Readiness Audit fast
- digital workers for back-office roles like research and reporting: a digital worker Pilot fast
- Alert triage for transaction monitoring and AML: agents that pull the context around an alert, cut false positives and draft the case narrative, with a person making every suspicious-activity call, via agentic AI development
- Human-in-the-loop review where a wrong call is a compliance incident: an Accuracy Gap Assessment fast
- AI consulting when leadership needs the roadmap first: an AI Opportunity Map fast
Proof from production
We do not ask fintech teams to trust a demo. The claims below come from systems that ran in production, on real customers and real documents. Two are public case studies; the document work is under NDA, so we describe it without naming the client.
- Instant biometric onboarding for a fintech: field onboarding went from about two days to about eight minutes, and works offline where connectivity fails
- Contract and property-report intelligence, built under NDA: long agreements and reports read and risk surfaced, with findings traceable to their source
- A needs-based advisory engine for insurance: a session that took about an hour now runs fully automated in under fifteen minutes, offline, with an audit trail on every recommendation
How an engagement starts
The first step is a founder-led 30-minute call, booked from the contact page through Google Calendar. Bring the workflow that hurts. We give an honest read on whether AI fits, what it has to beat, and which entry engagement proves it fastest. No discovery phase billed by the month.
- A 30-minute call with a founder, not a sales rep: bring the workflow, the volumes and the constraint
- A fixed entry engagement, a scoped engagement: an audit, a sprint or one workflow automated end to end
- A working system on your real cases, with accuracy reported as a number, before any commitment to scale
- An honest no-go if the data, the volumes or the economics do not support a build yet
What a fintech AI project needs
Most fintech AI failures are not model failures. They are missing structure: no audit trail, no PII boundary, no human gate on irreversible actions, no accuracy number anyone will sign. We build that structure in from day one, designed to support DPDP, GDPR, PCI DSS and SOC 2 expectations. We say designed to support deliberately: certification is your auditor's call, evidence is our job.
- Least-privilege access: the system gets the minimum it needs, in your accounts wherever possible
- Audit logs on every action, so an auditor's question has an answer
- PII masking before models see customer data, and data residency honored
- Cardholder data kept out of model prompts and logs where PCI DSS scope applies
- Human approval gates on high-stakes and irreversible steps
- An eval suite that reports accuracy per release, not per anecdote
- From your side: real sample cases, a named owner, and the number the system has to beat
If you build the software too
Much of fintech is software you build and have to trust: ledgers, payment flows, onboarding, the models that score risk. When AI writes that code, a clean-looking diff can still misprice, double-count or leak. everylayer, our evidence gate, scores what your tests actually prove across seven layers plus security, performance and accessibility gates, writes the missing tests as draft pull requests, and blocks unproven AI-written changes before they merge. It runs self-hosted inside your own network for strict PCI and data-residency boundaries, so your source never leaves your control.
- An audit trail on the code itself: what was proven, at which layer, before it shipped
- Contract tests that catch a schema change breaking payments or reconciliation before staging does
- Self-hosted, air-gapped deployment for PCI DSS and data-residency boundaries
- The same discipline as our digital workers, applied to your code: prove it before you trust it (/everylayer/)
Do you work with fintech companies outside India?
Yes. We have worked worldwide since 2019. Typical clients are funded startups and companies in the $10M to $100M range. Time-zone overlap and a weekly operating rhythm are part of the engagement design, not an afterthought.
How do you keep customer financial data secure?
Least-privilege access, audit logs on every action, PII masking before models see data, data residency honored, and human approval gates on high-stakes steps. The same design is built to support DPDP, GDPR and SOC 2 expectations. We do not claim certifications; we build the evidence your auditors will ask for.
Can the AI's decisions be explained to an auditor or regulator?
That is a design requirement, not a feature request. Every action is logged, document findings stay traceable to their source, confidence thresholds decide what routes to humans, and people sign off on high-stakes calls. The insurance advisory engine we built runs fully automated with an audit trail on every recommendation.
What can you show us from fintech production work?
Start with the instant biometric onboarding case: field onboarding cut from about two days to about eight minutes, offline-capable. Under NDA, we have built contract and report intelligence for document-heavy operations. Adjacent to fintech, our insurance advisory engine automated a roughly one-hour session to under fifteen minutes with full audit traceability.
Our fintech AI pilot stalled. Can you take it over?
Yes, that is a common starting point. Pilots usually die between demo and production because nobody built the boring half: evals, guardrails, audit logs, escalation paths, cost ceilings. We audit what exists, add that layer, and either productionize the pilot or tell you plainly why it will not work.
Which fintech workflows should we automate first?
High-volume work with clear rules and expensive exceptions: onboarding and identity verification, document review across KYC files and agreements, reconciliation and reporting, and first-line support triage. We score candidates by value, feasibility and risk, then start with one workflow and prove the delta before expanding.
Can AI help with transaction monitoring and AML without adding regulatory risk?
Yes, on the triage side, not the decision. The cost center in transaction monitoring is the false-positive rate: analysts clear the same benign patterns over and over. We build agents that gather the context around an alert, the KYC file, the history, the counterparties, and draft a structured case narrative, so an analyst decides faster. The system proposes; a person makes every suspicious-activity call, every step is logged, and the model never files a report on its own. It is designed to support your AML program and the audit behind it, not to replace the judgment a regulator expects from a human.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.