AI consulting · Financial services
AI consulting for specialty finance, payments and fintech operations
Native helps specialty finance, payments and fintech companies put AI agents into the operations work behind every account: credit applications, onboarding, servicing and reconciliation. The agents read the documents, apply your rules and hand your team a finished draft to approve. A first workflow goes live in weeks, and the whole operations function can work AI native within months.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 4 min read
Where AI has an impact in financial services
Credit application drafting
A full applicant pack turned into your own application format, with every figure traced to its source page.
Financial statement spreading
Several fiscal years spread into your template, with the balance sheet tied out before anything is generated.
Credit narratives
A written credit narrative drafted at the depth your analyst chooses, ready to edit.
Onboarding and KYB
Business applications checked for validity, not just presence, with the follow-up request drafted the same day.
Reconciliation
Bank, processor and ledger records matched daily, with each break explained instead of just listed.
Collections and servicing
Status questions answered from the system of record and dunning steps run inside the cadence you approve.
Dispute and chargeback packets
Evidence pulled from transaction and support records into the format each network expects.
Portfolio enrichment
Applicant and counterparty records enriched from your own portfolio tables and cited public sources.
Where AI has the most impact in financial services
Financial services firms already automate the transaction. What they still do by hand is everything around it: reading a dealer's tax returns, re-keying a merchant's application, matching a bank statement line to an invoice, answering a borrower who wants to know where their file is. That work is rules-heavy, document-heavy and high volume, which is exactly where agents do well.
The judgement stays with your people. Agents assemble, check and draft. Credit officers, risk teams and finance leads approve, and every approval is on record.
What agents take on, and what stays with your credit and risk teams
| Work | Agent | Your team |
|---|---|---|
| Credit application packs | Reads every document, spreads the financials, drafts the application | Reviews the draft and decides |
| Merchant and customer onboarding | Checks the file for completeness and validity, drafts the requests for what is missing | Approves the account |
| Reconciliation | Matches statements to the ledger and explains each break | Clears the exceptions |
| Servicing questions | Answers status questions from the system of record | Handles disputes and hardship |
| Anything outside policy | Refers with its reasoning written out | Decides |
Documents are the hard part, so we build for them first
The files that reach a finance operations team are rarely clean. Statements arrive scanned or faxed, manufacturer and dealer systems print their own coded formats, a tax return turns up where a balance sheet should be, and two documents disagree about the same number. An agent that only reads tidy PDFs fails on the first real file.
So every build starts with your real documents. Agents rank their sources (a filed tax return beats an internally prepared statement), check that the balance sheet foots before anything is generated, and flag any year where the scan quality is too low to trust. Every extracted figure carries its source page and a confidence rating, so an analyst can reconstruct any output.
What an AI engagement in financial services looks like
The first two weeks map one operation end to end: what arrives, who touches it, which checks are run and where the file waits. That map ranks the workflows by impact, and the first agent is usually the one that turns a document pack into a draft your team already knows how to review.
From there the work spreads across credit, onboarding and finance operations, built in your cloud accounts and wired into your loan, core or ledger system. The code, the data and the credentials stay in your name, and your team is trained to run and extend the agents. See how we work for the full sequence.
Case study · A captive equipment-finance lender
An equipment-finance lender cut dealer credit applications from days to minutes.
Native helped a captive equipment-finance lender turn each dealer's document pack into its own credit application. A draft that took an analyst three to four days now takes under fifteen minutes, and every figure traces back to the page it came from.
The challenge
Each dealer floorplan application arrived as a pack of eight or nine documents: financial statements, tax returns, credit reports, personal financial statements and earlier credit memos. Analysts keyed them into a two-sheet workbook by hand, reconciled documents that disagreed, and wrote the credit narrative from scratch.What we built
- An extraction layer that reads scanned, faxed and dealer-system statements, ranks sources so tax returns win a conflict, and flags any year with low scan quality.
- A spreading agent that fills the lender's own workbook across five fiscal years and refuses to generate anything until the balance sheet ties out.
- A narrative agent that drafts the credit write-up at three depths, enriched from the lender's portfolio tables and cited web research, for the analyst to choose and edit.
The result
Analysts start from a complete, checked draft instead of a stack of PDFs, and the decision on every application stays with the credit team.Frequently asked questions
What does an AI consultant do for a financial services company?
An AI consultant maps how documents and decisions move through your operations, finds where files wait, and builds agents that do the reading, checking and drafting inside your systems. At Native the same forward-deployed engineers who map the work build the agents and stay until they run in production.
Can AI make credit decisions?
AI can assemble the file, spread the financials, run the policy checks and draft the recommendation. The decision itself stays with your credit team, and the line between the two is written down before anything goes live.
How do you keep financial data secure when using AI?
Everything is built in your own cloud accounts, with models that do not retain your data, the narrowest access each agent needs, and a log of every action. See our security page for the controls.
Where should a lender or fintech start with AI?
Start with the document-heavy step your analysts like least, usually credit application drafting or onboarding review. The rules are known, the volume is steady, and the impact is visible within weeks.
How long does it take to put an AI agent into production in financial services?
A first agent typically reaches production within weeks. Taking a whole operations function AI native, from onboarding to servicing, takes months rather than years.