AI Credit Underwriting Agent

An AI Credit Underwriting Agent assesses each applicant on bureau and alternative data, cash flow, rent, utilities, not just a thin credit file, and prepares an explainable decision with reason codes. It scores and explains; the underwriter owns the approve-or-decline.

Tecla builds it inside your environment and runs it, wired into the bureau, alternative-data, and origination systems your team already uses.

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credit-underwriting · decision ready
APP Consumer loan · thin credit file, limited history
BUREAU Credit data pulled · 3 tradelines, no derogatories
ALT DATA Cash flow, rent, and utility history assessed
DECISION Recommend approve · reason codes attached
FAIR LEND Adverse-action logic and explainability recorded
READY Decision prepared, underwriter to approve
Reviewed on
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Excellent
4.8 out of 5
Trustpilot

The shift underway

Credit underwriting is moving beyond the thin credit file, using alternative data to assess applicants a bureau score alone would miss, and lenders are adopting it fast. The numbers show the shift.

44k–750k

more borrowers approved when alternative data supplements the credit file.

Source: Upstart / Zest AI analysis

25+ hrs

reduction in default rates reported with AI-driven credit underwriting.

Source: ChatFin

83%

of lenders plan to increase their generative AI budgets in 2026.

Source:

How it works

How the agent works

Underwriting credit means pulling the bureau, weighing alternative data, scoring the risk, and explaining the call, work that must be consistent and defensible on every applicant. The agent does that assessment and prepares the decision for the underwriter.

01

Bureau Pull

Pulls and structures the credit bureau data, tradelines, inquiries, utilization, and derogatories, for each applicant.

Reads the file
02

Alternative Data

Assesses cash flow, rent, utility, and other alternative data to evaluate thin-file and credit-invisible applicants a bureau score alone would miss.

Sees beyond the score
03

Risk Model

Runs your credit model to score default risk across many more variables than a traditional scorecard, applying your policy and cutoffs.

Scores the risk
04

Explainability

Decomposes each score into human-readable reason codes, so approvals and declines are adverse-action ready and defensible in a fair-lending exam.

Explains the call
05

Decision Draft

Prepares the recommendation, approve, conditional, or decline, with the rationale, ready for the underwriter to review and own.

Drafts the decision

Human in the loop

Every score carries its reason codes and the data behind it, so an underwriter can see why the agent recommended what it did. The agent scores and explains; the approve-or-decline, and the accountability for it, stay with the underwriter, not the model.

Scoped to you

This is a typical credit underwriting build. The exact parts are scoped to how your firm runs: your credit model and policy, the bureau and alternative-data sources, your adverse-action requirements, and where an underwriter signs off.

See what this would look like built around your portfolio operation.
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Related agents

How it reaches production

Built, run, and owned, one phase at a time

A credit model that scores a clean test set can still hide bias or fail a fair-lending audit on real applicants. A scoped brief, a named team, and someone accountable at each stage are what make this one dependable and defensible.

Sprint

Scope, fixed price

We map your diligence checklist, the workstreams in scope, your memo template, and the data room setup, then return a plan and a price before you commit.

AI Solutions Lead
Build

Built into your environment

Senior engineers assemble the agent inside your environment, wired into the data room, deal management, and document systems your team already works in.

AI Systems Lead
Run

Monitored and improved

The agent adapts as your checklist and deal types change, and we report against the metrics set at scope: documents reviewed, diligence turnaround, findings surfaced per deal.

Systems Lead + Engineers
Own

Yours, improving

The system is yours to keep, and it learns your firm's diligence standards over time rather than staying a generic document reader bolted on the side.

Your team

A build starts with a fixed-price Tecla Sprint that scopes and prices it before you commit.

‍ Book a scoping call
How it holds up

Trustworthy enough to make a credit decision on

A credit model that cannot explain its decisions is a fair-lending problem, not just an accuracy one. What makes this one dependable is that every decision carries reason codes, traces to its data, and leaves the call to an underwriter.

Reviewed by trajectory

Every finding cites the document

Each finding links back to the exact document and page in the data room, so an analyst can verify it before it reaches the investment committee. The agent surfaces; it does not assert what it cannot source.

Human in the loop

It reads, the deal team decides

The agent reads documents and flags risks, but what a finding means for the investment, and whether to proceed, stays with the deal team. It informs the decision; it does not make it.

Tuned over time

It learns your diligence standard

As your checklist, memo format, and deal types change, the agent is kept current, so its output matches how your firm actually runs diligence.

Start the build

Score every applicant, keep the credit call

A scoping call maps your diligence workflow and returns what the agent would handle, how success is measured, and the fixed-price Sprint to build it.

Talk to us

Fixed-price AI Systems Sprint. No commitment until scope is confirmed.

What deal teams ask first

What is an AI due diligence agent?

It reads a deal's full data room, works your diligence checklist, flags the risks, and drafts the findings in your firm's memo template, with every point sourced to a document. Tecla builds and operates the due diligence AI agent inside your environment, so the deal team starts from a drafted memo instead of a pile of PDFs, and owns the investment decision.

How is an AI due diligence agent different from a data room or VDR?

A data room stores the documents; a diligence tool helps you search them. This does the review: it reads every document, runs your checklist, and drafts the memo. Tecla brings due diligence automation to the analyst work itself, not just the storage, connecting the data room to the write-up your firm actually produces.

Does the agent make the investment decision?

No. It reads and drafts, but what a finding means for the deal, and whether to proceed, stays with the deal team. Everything the agent produces is a sourced starting point the analysts verify and the investment committee owns.

How do you keep the findings accurate?

Every finding links back to the exact document and page it came from, so an analyst can verify it before it reaches the IC. The agent surfaces and cites; it does not assert what it cannot source, which is the failure mode that matters most in diligence.

What does Tecla's due diligence build cover?

Tecla scopes the build to your firm: your diligence checklist, which workstreams the agent covers, your memo template, and how findings route to the deal team. Built for AI for PE due diligence or corporate M&A alike, it runs inside your environment and Tecla operates it.

What workstreams and documents can the due diligence agent handle?

Financial statements and QoE, customer and supplier contracts, cap tables, corporate records, and more, flagging customer concentration, change-of-control clauses, revenue-recognition issues, and unrecorded liabilities. The build maps the deal due diligence AI workflow your firm actually runs.

How is sensitive deal data kept secure?

The agent runs in your environment, integrated with your systems, under your own access rules. Data room and deal data stays within the boundaries your firm already answers to, and you own and control the system Tecla operates.

How long does a build take?

A build starts with a fixed-price Tecla Sprint that scopes and prices it before you commit. The Sprint maps your checklist, workstreams, and memo template, so the agent that follows fits how your firm actually runs diligence.

We have analysts and associates already. Where does the agent fit?

It takes the document reading and first-pass write-up off the team, so analysts spend their time on judgment and the deal, not organizing and validating data at 100 hours a week. Tecla builds and operates the due diligence AI agent alongside them.

How do we get started?

A scoping call maps your diligence workflow and returns what the agent would read and draft, how success is measured, and the fixed-price Sprint cost. Book a scoping call with Tecla to start.

Have any questions?
Schedule a call to discuss in more detail.
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