Illustrative impact based on published industry benchmarks — not results from a specific client.
Manual effort typically removed
Weekly hours many teams spend today
Cost per invoice best-in-class (vs $12-15 manual)
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.
Our approach
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.
Expected impact
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.
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.
Documents arrive
Invoices, bank statements and payment files land in every format: PDFs, scans, emails, exports. Today a person opens each one by hand.
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
0 hrs
per week
0 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.
Built with
Frequently 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.
Ready to build what's next?
Tell us about your project — we'll reply within one business day with a clear plan and a straight answer on fit.