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NSWS
Reconciliation & Finance Ops · Flagship

Reconciliation engines that close 80–95% automatically.

We build matching, classification, and exception engines for NBFCs and finance ops teams, across MIS, accounting, multi-bank statements, payment aggregators, and ledgers. Configurable, auditable, productionized.

Audit-trail by default
Multi-source ingestion
NSWS Reconciliation operator dashboard, synthetic UI mockup
Synthetic UI
Three use-case clusters

Real, delivered reconciliation engines.

Each cluster is a productized accelerator with its own data model, classifier rules, and exception flow, assembled from common matching and audit primitives.

Cluster 1 · NBFC

NBFC Loan Reconciliation

Multi-source matching across MIS (Collection / Disbursement / Closure), Day Book, multiple bank statements, and payment aggregator (PhonePe) data. Classification across Collection, Disbursement, Top-Up, Renewal, Adjustment.

  • Configurable matching weights (Loan ID 40, Name fuzzy 20, Amount 20, Date 10, UTR 10)
  • 5-class transaction classifier
  • Daily reco · monthly summary · exception · tag report
MISDay BookMulti-bankPhonePe
Cluster 2 · NBFC Field Ops

Cash Collection Reconciliation

QR-code-based collections vs bank ledger reconciliation, designed for NBFCs running merchant or field-collection ops. Handles batched settlements, T+1 posting, multi-merchant aggregation.

  • T+1 settlement reconciliation
  • Multi-merchant aggregation
  • Dispute flagging with audit trail
QR collectionsBank ledgerT+1
Architecture

Six-stage matching engine pipeline.

Each stage is configurable, observable, and auditable. Tolerances, weights, and classifiers are tuned per client without code changes.

01
Ingestion

MIS · Day Book · Bank · PhonePe · GRN · Invoices

02
Standardization

Name normalization · date format · amount sign · UTR extraction from narration

03
Matching engine

Weighted scoring 0–100 · configurable rules per use case

04
Classification engine

Rule-based + AI for fuzzy / edge cases

05
Exception management

Auto-flagging · audit trail per match

06
Reports

Daily · monthly · exception · tag · Excel export

Outcomes

Productionized accuracy across delivered engagements.

90–95%
Reconciliation accuracy
−80%
Manual effort post-deployment
100%
UTR-based dedup
5
Matching dimensions
Engagement model

Services-led from kickoff to handoff.

The platform you run is yours to operate and evolve. We bring senior engineers, productized accelerators, and accountability for production accuracy.

Phase 01
Discovery
1–2 weeks

Map your data sources, classification rules, exception policies

Phase 02
MVP build
4–6 weeks

Core engine for your top 1–2 use cases

Phase 03
Scale-out
4–8 weeks

Additional use cases, dashboards, integrations

Phase 04
Support
Ongoing

Rule tuning, accuracy monitoring, audit response

Frequently asked

Reconciliation engineering, common questions.

What NBFC CTOs and finance ops leaders ask before scoping a reconciliation engagement.

  • How long does it take to deploy a reconciliation engine?

    Discovery 1–2 weeks (data sources, classification rules, exception policies). MVP for top 1–2 use cases: 4–6 weeks. Scale-out to additional use cases and dashboards: another 4–8 weeks. Most NBFCs see meaningful auto-match rates within 6 weeks of kickoff.

  • What's the realistic auto-match rate we can expect?

    Across delivered NBFC engagements the matching engine hits 90–95% auto-match accuracy after tuning. The remaining 5–10% routes into a clean exception queue with audit trail per match, not a spreadsheet. Manual reconciliation effort typically drops by ~80% post-deployment.

  • Can the engine work with our existing data sources or do we need to migrate?

    Source-agnostic. We've integrated MIS (Excel/CSV), SQL databases, bank statement formats (PDF/Excel/CSV), payment-aggregator APIs (PhonePe, Paytm, Razorpay), and accounting systems (Tally, BUSY). Adapters at the boundary mean you don't standardize source data first.

  • How are exceptions handled, and do they require ops headcount?

    Exceptions auto-flag with matching score, criteria used, and source row references, then route to a queue ops resolves in batches. Most clients reduce reconciliation headcount by 60–80% post-deployment, redeploying staff toward dispute resolution and data quality rather than spreadsheet stitching.

  • Is the matching engine RBI / DPDP / IRDAI compliant?

    Engineered for audit-trail-by-default, RBAC, and data residency. Per-match evidence (score, criteria, source row references) satisfies inspection. We deploy in alignment with RBI IT Outsourcing norms and DPDP Act 2023 data handling.

Start the conversation

If your team is spending 4+ days a month on reconciliation, the math has already broken in your favor.

Share your data sources and current matching pain points. We'll deliver a 1-page assessment with a recommended matching engine architecture.

  • A senior engineer reviews your submission — not a sales rep
  • Response within one business day
  • NDA available before scoping if needed