Financial services

A faster month-end close, with every step logged

Reconciliation, the monthly close and audit prep follow clear rules, yet people still do them by hand. We automate that work and keep a full record for your auditors.

Problems you’ll recognise

Where the close gets stuck

A close that takes most of the month

Four analysts and hundreds of manual reconciliation steps. A discrepancy from day 3 still turns into a scramble on day 10.

Median month-end close for mid-sized companies: 6–8 days (APQC benchmarking).

Audit prep as a project

Pulling records, compiling reports and cross-referencing by hand turns every audit into a multi-week project. The data is already there, and people shouldn’t have to assemble it.

Compliance reports that lag behind

Regulatory reports, VAT reconciliation and cost allocation reuse the same data, reformatted for each recipient. Done by hand, they tend to be a little late and a little inconsistent.

What we built for a finance client

Inside the month-end close we automated

Recurring tasks and reports · Finance

Before: The close took four analysts most of the month, and 30% of entries needed rework because they didn’t match the first time.
What we built: A reconciliation engine that pulls in transaction feeds, matches entries across accounts, flags exceptions for review and produces audit-ready reports on schedule.
Result: Reconciliation runs at 99% accuracy, and the analysts get their days back for work that needs them.

12 → 4 days to close the month

Saved per year€175K

Show the calculation

8 working days recovered per close, across 4 analysts, every month: 384 analyst-days a year back on real work. At €365/day that’s €140K, plus €35K of rework and restatement avoided. Payback: about 9 weeks.

Read the full case

The service behind it

What the reconciliation engine does

This is our recurring tasks and reports service, set up for finance teams, where an audit trail matters as much as speed.

Pulls in bank feeds, ERP data, payment processors and uploads, and lines them up in one format before matching.
Your matching rules handle the clear cases across accounts and general ledger (GL) codes, and the AI layer catches patterns the rules miss.
Close reports and reconciliation summaries are produced on schedule, each with a timestamped record of what matched and what was flagged.
Every change, automated or human, is logged with time, user and reason, and can’t be quietly edited.

Technical details

Python / pandas

Cleans and matches the transactions

Azure OpenAI

Sorts the messy cases and exceptions

PostgreSQL

Stores transactions and the audit log

FastAPI

Connects to your ERP and delivers reports

Celery + Redis

Runs the close on schedule

React (exception dashboard)

Where your team reviews flagged items

Free 30-minute call

How long does your month-end close take?

Tell us your close cycle, your team size and the systems you use. We’ll scope what’s realistic to automate and give you a number.

“You talk to me, the engineer who builds your system. If AI isn’t the right fix for your problem, I’ll tell you.” Ayman Santeer, Founder

Book a free call

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contact@elanova.ai +31 6 40747312 WhatsApp