Case study · Audit · Power Automate · Python
Audit firm — 75% less analysis time, 3.4-month payback
- after year one
- 354% ROI
- cut in accounting analysis time
- 75%
- analyst hours saved per year
- 780 h/rok
- implementation payback
- 3,4 mies.
A Polish statutory-auditor firm spending 4 hours a day on manual accounting analysis. Power Automate + Python cut the cycle to 60 min, saved 780 h/year, and returned 275–354% ROI in year one.
Context
A statutory-auditor firm on the Polish financial market, focused on financial-statement audits, ledger checks, liquidity analysis and RFPs from corporate clients. Headcount 1 to 10, single shift, expert team — statutory auditors backed by analysis specialists.
Technology before the audit: Microsoft 365, Microsoft SQL Server as the operating database, OneDrive as shared space, Windows on workstations. No integrated ERP, no flow automation, no central analytics platform.
Industry: Financial audit, statutory-auditor firm. Size: 1–10 staff, single shift. AiP Audit variant: Start-up Express. Engagement: AiP Audit + implementation (about 4 months together).
Challenge
The AiP Audit found four critical processes that together generated 80% of the team’s time load.
First, accounting-data analysis and correctness checks — a statutory auditor spent 4 hours a day pasting data from JPK and Excel files, comparing statements, spotting deviations and documenting findings.
Second, sample verification in the financial-statement audit — matching invoices and payments to orders and to the ledger. High repeatability, standard inputs (PDF, Excel, scans), 100% accuracy required and a full audit trail.
Third: liquidity analysis as part of going-concern assessment. Hand copy-paste across formats (PDF, Excel, JPK, industry data) over 3 years, manual ratio calculations, Excel charts. 45% of process time went to data prep, 35% to hand calculations.
Fourth: client enquiry handling — 80 hours a month on email, 40 hours on phone, 120 RFPs a month. No structure, no CRM, no priority.
Together: 1 000 expert hours a year, at 150–200 PLN an hour, that is 150 000–200 000 PLN yearly cost of work most of which did not need expert judgement — routine automation would have been enough.
Approach
01 — Process scoring and automation potential. The first two audit weeks went to a diagnostic questionnaire, process scoring on QA10’s model (5 criteria: Automation potential, Ease of implementation, Business value, Urgency, Risk) and Quick Wins. Process 1 (accounting-data analysis) scored highest on high repeatability and structured inputs.
02 — Three architecture variants. For each critical process we designed three budget variants: low (Power Automate + Python, 37–47 K PLN year 1, 75% cut), mid (UiPath + Azure AI + Power BI Premium, 59–64 K PLN, 81% cut), Enterprise (Dynamics 365 BC + Azure AI + KSeF, 120–125 K PLN, 92% cut). Each with ROI, payback, hour and money savings.
03 — Recommendation and choice. We recommended the low variant — not because it is cheapest, but because at this firm’s scale it gives the highest ROI (275–354% in year one) and the fastest payback (3.4–3.8 months). Enterprise would be justified only above 20 people with a larger audit portfolio. The client accepted.
04 — 4-week implementation. Four weeks: kick-off and environment setup (week 1), Power Automate + Python scripts (week 2), report templates and Excel + Power BI dashboards (week 3), UAT, training and go-live (week 4). After go-live the QA10 team ran 4 weeks of hypercare.
Stack
Automation core: Microsoft Power Automate for process orchestration — automatic file import from OneDrive, new-data monitoring, triggers for analysis processes, structure validation, error alerts. Python 3.10 for data work (pandas for analysis, numpy for calculation, openpyxl and xlrd for Excel, reportlab for reports) and comparison logic (cleaning, statement matching, anomaly detection). Microsoft Excel + Power BI for the report layer: templates, dashboards, visuals, export. OneDrive Business as file store (1 TB, seven functional folders). All infrastructure on the client’s existing Microsoft 365 licences — zero new server licences, zero new hardware.
Results
| Metric | Value |
|---|---|
| Accounting-analysis time cut (4h → 60 min/day) | 75% |
| Expert time saved (rate 150–200 PLN/h) | 780 h/year |
| Year-one financial savings | 130–168 thousand PLN |
| Payback (implementation cost 37–47 K PLN) | 3.4 months |
| Year-one ROI (net) | 275–354% |
| Typical accounting-analysis cycle after | 60 min / 4 h |
275–354% ROI in year one at this firm means every zloty spent on implementation came back three times before the end of year one. That is the highest ROI among QA10 deliveries in the last twelve months — in context: small firms have an unfair advantage, because small operating scale means every specialist hour returned to expert work weighs more in the economics than in a large organisation.
The most important effect is not financial. A statutory auditor who used to lose 4 hours a day on manual checks can now spend those hours on audit-risk judgement, substance analysis, client conversations. No automation replaces that — automation did not replace the expert; it freed capacity for work only an expert can do.