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NeuralYug

Work · Fintech · Nepal

AI invoice and reconciliation automation for fintech

A blueprint for how we would automate invoice matching and reconciliation so a finance team spends minutes reviewing, not days re-keying.

blueprint18 Jun 2026
50%
Manual effort typically removed
~10 hrs
Weekly hours many teams spend today
$5
Cost per invoice best-in-class (vs $12-15 manual)

This is a solution blueprint — a reference architecture we can build for your business. The figures above are cited industry benchmarks for this class of system, not results claimed for a named client.

The pipeline we'd build

4 stages

Stage 01 · Invoice

Documents land in one place instead of several inboxes.

The challenge

In a lot of fintech and finance teams, reconciliation is still a manual grind. Invoices, bank files and ledger entries arrive in every format, and someone matches them by hand each week. Industry surveys back this up: about 56% of finance teams report spending more than 10 hours a week processing invoices and payments, and manual invoice handling commonly costs $12 to $15 per invoice. That is slow, error-prone, and it buries skilled people in copy-paste work.

What we did

This is how we would build it. Documents land in one place and a document-AI model reads each one, pulling out the amount, date, party and invoice number with a confidence score on every field. A matching layer then pairs each invoice with the right payment and ledger entry automatically. Clean, high-confidence matches pass straight through. Only the genuine edge cases (a mismatch, a low-confidence read) reach a person, who approves or corrects them, and the model learns from that correction. A person keeps judgement; the machine does the repetitive part.

The outcome

Based on published benchmarks, automation like this commonly removes 40 to 60% of the manual effort and cuts cost per invoice by 60 to 80%. Best-in-class teams process an invoice for around $2 to $5 versus $12 to $15 by hand, and cycle times drop from roughly two weeks to a few days. For a Nepali fintech, the practical win is simple: the weekly bottleneck becomes a short review, the close speeds up, and the finance team gets its time back for work that actually needs a human.

Stack

Next.jsDocument AI / OCRPythonAutomation pipelinesPostgreSQLCloud (AWS or GCP)

Reconciliation is one of those jobs that quietly eats a finance team's week. Here is a blueprint for how we would automate the grind while keeping people in charge of the calls that matter.

The reconciliation pipeline

Follow one invoice from inbox to reconciled. Tap a stage to see what happens.

Solution blueprint

Documents arrive

Invoices, bank statements and payment files land in every format: PDFs, scans, emails, exports. Today a person opens each one by hand.

AI does the workA person owns judgement
The pipeline: documents in, reconciled books out. Tap a stage to see what happens there.

What the numbers look like

The sliders below are seeded with published benchmarks. Move them to your own volume to see the hours a team could reclaim. It is illustrative, not a promise.

Hours a finance team could reclaim

Move the sliders to your own numbers. The saving is industry-typical, not a promise.

Reclaimed

5 hrs

per week

230 hrs / year

Illustrative, using published benchmarks. Sources: Business Initiative CFO survey (56% of teams spend 10+ hrs/week); AP automation studies showing 60-80% cost-per-invoice reductions. Your results depend on volume and data quality.

Illustrative time reclaimed, using industry benchmarks. Your results depend on volume and data quality.

At a glance

Client

Solution blueprint

Sector

Fintech · Nepal

Service

Flow Automation

Kind

blueprint

Headline result

50% · Manual effort typically removed

Handover

Documented, tested code in your repository

Questions we were asked

Are these numbers results NeuralYug delivered for a client?

No. This is a solution blueprint, not a past engagement. Every figure is a published industry benchmark framed as what is typically achievable, not a result we have delivered. Your own numbers depend on your volume and data quality.

Does the AI replace the finance team?

No. The model handles the routine matching so people stop re-keying. A person still owns judgement and reviews the edge cases the system flags, and their corrections make the model better over time.

How does it handle a document it is unsure about?

Every extracted field gets a confidence score. Anything low-confidence or mismatched is routed to a human reviewer with the evidence attached, rather than being posted automatically.

Same problem, different business?

We'll send the architecture and a realistic timeline for your version of this — no obligation.

Request a blueprint