Financial reporting: making your data reliable for trustworthy steering

Case study: how a European fintech reduced closing time and reporting errors through finance data governance.

L'équipe DATALIA · Publié le 11 août 2026 · Mis à jour le 11 août 2026

Réponse directe — Contexte, action, résultat (45–55 mots) : Une fintech européenne consolidant 40 entités industrialise la collecte des écritures et automatise les rapprochements. En six mois, elle a réduit le temps de clôture mensuelle, diminué les ajustements manuels et fiabilisé les comptes, améliorant la capacité du contrôle de gestion à produire des reportings exploitables chaque mois.

Context

The client is a fast-growing European fintech with centralized accounting but financial flows spread across 40 legal entities. The finance team produced largely manual monthly reports: CSV extracts, Excel macros, and reconciliations done by hand. The CFO wanted a single source of truth for consolidated reporting and operational KPIs.

Problem and objectives

The problem: heterogeneous data, inconsistent nomenclatures and undocumented reconciliation rules. Management control was losing time correcting discrepancies and rebuilding historical series.

Measurable objectives set at the start:

• Reduce monthly close time by more than 30% within 6 months.
• Cut manual reconciliation adjustments by a factor of 3.
• Obtain full traceability of sources for every reporting line.
• Standardize the consolidated chart of accounts and automate invoice/payment reconciliations.

The solution implemented

The solution combines data governance, reproducible transformation rules and targeted automation. Here is the intervention framework and the operational steps we implemented.

1. Quick diagnosis and scope (Vision & Analysis)

We mapped the sources (local ERPs, PSPs, bank files). Goal: identify the 20 tables and flows that caused 90% of discrepancies.

Deliverable 1 — Flow prioritization grid (standalone):

Objective: Prioritize flows to automate to reduce critical errors.
To collect: ERP exports, bank statements, close frequency.
Method:
- List incoming flows and key fields.
- Measure error frequency and time spent per flow.
- Rank by financial impact and frequency.
Output: Prioritized table [CSV] with Impact×Frequency score.

Note: usable in a scoping meeting. Won't work if exporters refuse to share raw files.

2. Standardizing the reference data

We defined a consolidated chart of accounts and standard mappings (analytic codes, cost centers). Each transformation rule is versioned.

3. Automating extractions and transformation (light ETL)

We automated extraction via secure connectors, transformed the data with audited scripts and stored an immutable landing zone. Transformations are testable and traced.

4. Reconciliation rules and auditability

Reconciliation rules are coded (join key, tolerance, assignment logic). Each reconciliation produces an audit log accessible to management control and internal audit.

5. Dashboards and human validation

Reports are produced automatically, but exception validation remains human. A digest email flags unresolved cases and quantifies their impact.

Deliverable 2 — Time savings calculation template (standalone)

Objective: Estimate time savings after automation.
To collect: average time per task, monthly frequency, hourly cost.
Method:
- Calculate total_time_before = Σ (task_i × frequency_i).
- Estimate expected automation rate per task.
- Estimate total_time_after = total_time_before × (1 - average_rate).
Output: Estimated hours saved per month and avoided cost.

Note: useful to convince a CFO. Limitation: assumes automation rates are achieved and historical data is properly recovered.

Results

We measured results over 6 months, scope: consolidation of closing accounts, invoice and bank reconciliations, client's internal operational scope.

Measurement method: anomaly ticket database, finance team timesheets and reconciliation audit logs. These figures are field observations from a DATALIA project with a European fintech.

What didn’t work

Some points slowed the project:

• Poor quality of historical exports: some providers didn’t supply persistent IDs, making retroactive reconciliation costly.
• Change resistance from some local teams: fear of losing control over specific entries required additional workshops.
• Rare cases of discrepancies due to undocumented business rules: these exceptions required building an exploitable exceptions repository.

Fixes applied: addition of a probabilistic matching module, targeted training and implementation of an exceptions register validated by management control.

Key lessons

• Automating the most frequent flows pays off fastest: focus on the 20/80.
• Traceability is more important than automation: without logs and versioning you lose the CFO’s trust.
• A quantified deliverable (hours saved, cost avoided) is the decisive argument for budget approval.
• Keeping humans in the loop for exceptions protects operations: automation should reduce decision volume, not eliminate it.

Actionable advice for a CFO

These actions can be applied internally to reduce the risk of a costly, ineffective project.

• Start with a 2-day audit: list the 10 sources that feed 90% of reporting.
• Measure time spent on reporting: use the Time savings calculation template above to quantify potential savings.
• Standardize the reference data: a consolidated chart of accounts and mandatory mappings per entity.
• Automate in waves: run the first wave on the most repeatable scope (e.g., bank reconciliation).
• Enforce traceability from the start: every transformation must generate a log understandable by an auditor.

Role of DATALIA

We support finance teams from diagnosis to production. DATALIA combines audit, integration and training: we design mappings, automate extractions, and train your controllers on validating exceptions. For sovereign AI projects and orchestration of financial flows, DATALIA.App is deployed in the client environment to keep data under control.

For more on our approaches and references, see our dedicated page or contact us via https://www.datalia.app/.

Limits and reproducibility conditions

This model works when:

• sources expose stable identifiers or accept the implementation of a business identifier;
• teams accept minimal governance (data ownership and extraction SLAs);
• the initial scope remains limited to priority flows (don’t try to automate everything in the first iteration).

What the method does not solve: strategic transformation of the business model, tax optimization or legal recommendations: those topics require dedicated specialists.

Frequently asked questions

How long does it take to see the first gains on reporting?

In our projects, the first wave of automation (extracts + mappings for 2–3 critical flows) shows visible gains within 6 to 12 weeks. The ratio depends on the quality of exports and the ability to formalize business rules.

Can GDPR compliance be ensured while automating financial data?

Yes. Security and data minimization are integrated from the design stage. We encrypt links, limit retention of sensitive data and produce traceability usable by your DPO. For the legal framework, the CNIL details applicable principles (refer to official documentation).

Automate your business with AI thanks to DATALIA: DATALIA →