Avexo Credit · India

Manual underwriting doesn't scale. Neither does quality.

The underwriting and analytics infrastructure for lenders in India, from the first lead to the loss you did not see coming. It sources sharper, underwrites deeper, and shows you where the book is stuck.
Enabling technology. The credit call stays with you.

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The whole book

One line runs the whole book. We instrument all of it.

Every lender already runs this funnel. Avexo puts purpose-built agents along it, one job each, so nothing goes dark after the loan is booked, and you can see exactly where the book leaks: in the leads you waste, the files you underwrite thin, or the risk that slips through after disbursal.

01LeadPre-qualify and route the enquiry

02UnderwriteScore against your policy, with the full data picture

03DisburseDecision and drawdown, with an audit trail

04MonitorTrack the portfolio and lead quality as it moves

05InsightAsk why. Get an answer grounded in your own data

Lead intelligenceSales
A lead-intelligence agent fills the funnel with prospects that fit your policy.
UnderwritingRisk
Reads the full data picture, applies your rules, and decides with a complete trail.
Analytics · agentsStrategy
Purpose-built agents ride the book, so you can interrogate it in plain language.

What's broken

Depth-limited underwriting, applied unevenly.

The losses don't come from a shortage of good borrowers. They come from underwriting that runs only as deep as the analyst who happened to open the file, on cases that looked clean on the financials.

Depth depends on who opened the file. A senior underwriter checks a dozen things, including whether the top customers and suppliers are real and whether the director is selling to himself. A junior checks half that and skips the network layer, because it takes hours and no form requires it. Same ticket, different output. The gap shows up in DPD three months later.

The most important checks are the least auditable. AML screening, counterparty cross-checks, supplier identity: done differently by every analyst, logged inconsistently, often not logged at all. When a file turns bad, the trail on what was actually checked is thin, and that is not an answer for your board or your regulator.

Scaling means hiring, not optimising. Every file is hours of lookups and manual checks. To do ten times the volume at the same quality, you need close to ten times the people. There is no other path under the current model.

Why it matters One file a quarter that clears where a thorough senior's supplier and related-party check would have flagged it is enough to erase the margin on the hundred that went right. One in a hundred is all it takes.

The solution

Your pipeline, optimised at the lead stage and the file stage.

The data your team collects by hand, assembled automatically and read against your policy, with the network layer most stacks skip built in.

The full data picture, automated. Every source your team checks today, plugged in and cross-referenced: company registries, commercial and consumer bureaus, banking and transaction data, tax filings, and litigation and watchlists. No manual lookup, no step that depends on the analyst remembering to check.

A proprietary network model. Built to read the whole supply chain around a borrower, not the borrower in isolation: related parties, counterparty concentration, and circular trade. This is the layer most underwriting stacks skip because it is slow to do by hand.

Product matching, not just approve or decline. The same scoring layer checks a lead against every product's risk appetite, so a lead too risky for one facility can still be a clean approval for another, instead of being declined and lost.

Lead-stage pre-scoring. The same data layer runs at intake, before a file reaches an underwriter, so the pipeline is prioritised by likely approval and indicative limit, not first in, first out.

What we cover

Every product on your shelf, underwritten.

We help you source, underwrite, monitor, and shape policy across the products you already run, mapped to the names your market actually uses. Secured or unsecured, consumer or business.

Source Underwrite Monitor Shape policy

Term loansbusiness and MSME, secured or unsecured

Working capitalcash credit and overdraft facilities

Invoice and bill discountingreceivables finance

Supply-chain financevendor and dealer programmes

Loan against propertyand other secured facilities

Equipment and asset financeplant, machinery, vehicles

Merchant cash advanceturnover-linked funding

Unsecured business and consumerpersonal and small-ticket loans

Real impact, fast

Live in three months. The numbers move from there.

You are not buying a research project. We fit the specialised agents to your policy and your book, the impact shows up in the metrics that matter, and you keep the infrastructure to build on.

+15%lead-to-disbursal ratio

-80%underwriting turnaround time

3 mofrom kickoff to live

A fintech lender, around $300M in assets under management.

How

The agents already exist

Because the lead-intelligence, underwriting, and analyst agents are already built and calibrated, we adapt them to your policy and your data instead of starting from a blank page. That is how three months is possible.

Yours to keep

You own the infrastructure

We fit the agents to your book, hand you the AI infrastructure, and train your team to run it. You get a foundation to build further on later, not a black box you rent forever.

Pricing

You pay for the result

You pay half up front. The other half is due only when the agreed KPI is delivered. If the result does not land, you do not pay for it.

See a real underwriting run and the numbers.

The data sources for India, real sample cases, and the full proposal are shared privately on request.

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