Financial reporting: structure your data to steer performance

Case study: how a European fintech reduced its closing times and made its reporting more reliable by centralizing its financial data.

Quick answer : Centralizing financial data and automating consolidation reduces close time, improves the accuracy of statements and makes the cost of reporting measurable. In this case, a fintech shortened its monthly cycle and cut down manual corrections through data governance and business connectors.

Context

Client: a mid-sized European fintech, multi-product activity, 120 employees, operations across several countries. The finance department prepared the monthly reporting from Excel files coming from 7 sources (accounting, treasury, CRM, payment platform, business spreadsheets, sales ERP, tax reporting). Consolidation took 8 to 12 days, with frequent manual rework.

Issue and objectives

Main problem: fragmented data, inconsistent business definitions and a manual consolidation process causing errors discovered late.

• Objective 1 — Reduce the monthly close time in a measurable way.
• Objective 2 — Reduce post-close corrections and adjusting entries.
• Objective 3 — Achieve traceability and a full cost of reporting to make investment trade-offs.

The solution implemented

We structured the engagement into four operational steps, each delivering a standalone deliverable usable by the finance department.

Step 1 — Diagnostic and flow mapping (Deliverable: prioritization grid)

Goal: identify sources, account definitions and re-entry points.

Method:

• Audit of the 7 sources and input processes (interviews, extraction of sample files).
• Mapping of value points (where financial decisions are made).
• Prioritization grid of cases to automate (number of records × frequency × hourly cost).

Output: a quantified grid presenting the top 10 processes that are worth automating. This deliverable helps defend a budget before the CFO or the executive committee.

Step 2 — Data model and reconciliation rules (Deliverable: data dictionary)

Goal: harmonize definitions (local charts of accounts, multi-currency flows, allocation rules).

Method:

1. Definition of a minimal canonical model (accounts, cost centers, projects, currencies).
2. Writing transformation and reconciliation rules (mapping, validation rules, exception thresholds).
3. Deployment of a shared dictionary accessible to finance and operations teams.

Output: a data dictionary with example mappings and annotated exception cases. This deliverable reduces ambiguity between accounting and business teams.

Step 3 — Integration and automation (Deliverable: test checklist)

Goal: connect sources to the consolidation engine and automate recurring reconciliations.

Method:

• Choice of connectors: native APIs or ETL extractors depending on the source.
• Implementation of a data pipeline that logs imports and versions files.
• Automation of reconciliations and queuing of adjustment entries for human review.

Output: a test checklist (unit tests, integration tests, alert thresholds). The checklist is designed so that financial controlling validates the production switch.

Step 4 — Governance, costs and steering (Deliverable: full cost model)

Goal: make financial reporting measurable in cost and benefit.

Method:

• Calculation of the current full cost of reporting (hours × rates, licenses, rework).
• Projection of the solution TCO (licenses, integration, maintenance, training).
• Comparative table of costs / gains over 24 months.

Output: a full cost model with variable assumptions. This deliverable is used by the CFO to compare integrator quotes.

Results

Field observation: over a 6-month pilot period, the fintech migrated the consolidation scope for product accounts and treasury. The following measurements were recorded by internal financial controlling.

These figures come from an internal measurement at the client and from our financial model applied to the automated scope. They serve as a discussion basis but should be adapted to your organization.

Comparison table: technology options

What didn't work

Common mistake: trying to automate everything at once. We observed that the fintech initially attempted to migrate 100% of flows in a single iteration. Result: delays, business misalignment and budget overruns.

Corrective action applied: break the project into waves (80/20), automate high-volume/low-exception processes first, then progressively handle complex cases.

Key takeaways for a CFO / management control

• Measure the current cost of reporting before choosing a solution. A defensible figure internally wins most budget decisions.
• Prioritize high-volume, low-exception processes. They provide the quick ROI needed to fund the rest.
• Maintain traceability and history of imports: this reduces time spent diagnosing discrepancies.
• Include financial controlling from the data model definition phase. The data dictionary prevents disputes between accounting and business.
• Ask vendors for a full cost model (data migration, maintenance, licenses, training).

Actionable advice

Here are four concrete actions you can start within 30 days:

• Take an inventory of sources and measure time spent per file (hours × frequency).
• Build a grid prioritizing processes by financial impact ([VOLUME] × [FREQUENCY] × [COST_H_PER_HOUR]).
• Request a pilot on a reduced scope (treasury + accounts receivable) and require the test checklist.
• Prepare the question for the committee: current reporting cost vs projected TCO over 24 months.

Operational deliverables to reuse

Deliverable 1 — Prioritization grid (output: ordered list of processes to automate)

Objective: rank reporting processes to automate.
To gather: list of sources, lines volume/month, processing time per file, exception rate.
Method:
- For each process: calculate Impact = VOLUME × FREQUENCY × COST_H_PER_HOUR.
- Rank by descending Impact.
Output: Top 10 processes to automate.
Note: works if you have measurable volumes; otherwise start with a time audit.

Deliverable 2 — Full cost model (output: TCO and break-even)

Objective: compare current cost vs projected cost and compute break-even (months).
To gather: annual hours spent, average fully-loaded salary, integration quotes, annual licenses, training cost.
Method:
- Current_cost = annual_hours × fully_loaded_salary.
- Projected_cost = initial_investment + annual_licenses + maintenance.
- Break_even_months = initial_investment / expected_monthly_savings.
Output: 24-month comparative table + sensitivity +/- 20%.
Note: variables in [BRACKETS] to adapt; present to the CFO for validation.

Role of DATALIA

We help finance teams turn a vague symptom ("we lose time on reporting") into a quantified, prioritized project. In this case, we delivered the mapping, the data dictionary and the automation pilot. Our approach focuses on one deliverable per step that makes cost and benefit defendable in committee. For product information and use cases, consult our dedicated page or request an operational audit.

Conclusion

For a finance department, financial reporting is more than a tool: it's a measurable process. Centralizing data, harmonizing definitions and progressively automating reconciliations make reporting faster, less costly and more actionable for management. In practice, a successful project is understood by three numbers: close time, hours saved and cost avoided. Start by quantifying these three elements and prioritize according to a full cost model.

Frequently asked questions

How long does a consolidation pilot take for a reduced scope?

A pilot on treasury + accounts receivable typically deploys in 6 to 8 weeks: diagnosis, connector setup, testing and controlled acceptance. Duration varies depending on the quality of source exports.

Does automation eliminate the need for a finance team?

No. Automation removes repetitive tasks and frees up time for analysis. The finance department refocuses on steering, exception controls and financial strategy.

Book your call and your free audit today with a DATALIA expert.

https://www.datalia.app/

The DATALIA team