AI and Financial Data: How a Machine Makes Decisions
A retail company automated the consolidation of its financial flows with a sovereign AI, reducing month-end closing times by 70% and data entry errors by 90%
A retail company automated the consolidation of its financial flows with a sovereign AI, reducing month-end closing times by 70% and data entry errors by 90%.
The DATALIA team · Published August 15, 2025 · Updated August 15, 2025
In accounting and finance departments, teams spend hours chasing documents, verifying amounts, and correcting data entry errors. However, an AI system properly integrated into your existing systems can make these decisions on its own — with full audit trail.
Direct answer
An AI connected to existing financial data enables automating up to 80% of repetitive tasks (bank reconciliations, invoicing, reconciliations). It reduces month-end closing times by 50 to 70%, data entry errors by 80 to 90%, and frees teams for higher-value analyses.
Table of contents
Context
Novateo Distribution, a subsidiary of the Novateo group, manages a portfolio of 1,200 clients in the construction materials distribution sector. Its accounting department, composed of 8 employees, processes over 3,000 invoices, 1,500 delivery notes, and 800 bank reconciliations each month.
The monthly closing process took an average of 12 business days, with an error rate of 6% and team occupancy rates nearing 100%. Internal and external follow-ups consumed 20 hours per week.
Problem statement and objectives
The objectives set by the finance department were clear:
- Reduce the monthly closing time to 5 business days.
- Decrease the data entry error rate to below 1%.
- Free up 15 hours per week per employee for analysis tasks.
- Ensure GDPR compliance and traceability of sensitive data.
These objectives had to be achieved without any increase in budget or staffing.
The solution implemented
DATALIA integrated DATALIA.App, a sovereign, private, and self-hosted AI, directly into Novateo's infrastructure. The AI was connected to existing systems (Odoo ERP, invoicing software, and banking platform).
Key steps
- Process audit: mapping of the 12 repetitive processes consuming more than 10 hours per week.
- Integration: connecting the AI to accounting and banking databases via secure API.
- Configuration: training the AI on 6 months of historical data to recognize invoice patterns and bank discrepancies.
- Progressive deployment: rolling out in waves, starting with bank reconciliations.
- Monitoring and adjustment: weekly review of decisions made by the AI, with human validation for atypical cases.
Each decision made by the AI is recorded in an audit log, accessible to management controllers and accountants.
Results
| Indicator | Before | After | Gain |
|---|---|---|---|
| Monthly closing time | 12 days | 5 days | -58% |
| Data entry error rate | 6% | 0.8% | -87% |
| Hours freed per week per employee | 0 hours | 16 hours | +16 hours |
| Number of invoices processed per day | 50 | 120 | +140% |
| Internal follow-ups (hours/week) | 20 hours | 5 hours | -75% |
Results were measured over a 6-month period, from March to August 2025. The error rate was verified by an independent external audit firm.
What didn't work
The first version of the AI failed to correctly recognize invoices from two suppliers using low-quality scanned PDF formats. This resulted in a 15% rejection rate for these documents, requiring manual intervention.
In addition, the initial integration took 3 weeks instead of the planned 10 days, due to insufficient documentation for the existing invoicing software APIs. The deployment had to be redesigned to isolate these data flows and process them upstream.
Finally, a temporary third-party collaborator interrupted the process by manually modifying a file while the AI was in learning mode, corrupting 2 days of data.
Key lessons
- Start with the most stable processes: bank reconciliations, while repetitive, have a clear structure, which facilitates AI learning.
- Plan for a human review mode: 100% automation is neither realistic nor desirable. Plan for 5% of cases to be manually validated.
- Clean data before integration: heterogeneous or incomplete data can disrupt learning. Initial cleanup saves time.
- Host locally to ensure sovereignty: by avoiding the transmission of financial data to third parties, the company avoided risks associated with shadow AI.
- Plan a compliance audit: a GDPR and ISO 27001 review validated the traceability of AI decisions.
Conclusion
This case study shows that a sovereign AI, when properly integrated, can transform the accounting department of a mid-sized company. Novateo not only achieved its numerical targets but also redefined the role of its teams: moving from data entry to strategic analysis.
The success is built on three pillars: gradual integration, full traceability of decisions, and human oversight on exceptions. This model is reproducible in any organization with standardized financial processes and a commitment to modernizing its practices.
Frequently asked questions
Can an AI really make financial decisions autonomously?
Yes, for repetitive tasks such as bank reconciliations or transaction classification. For complex decisions, the AI provides suggestions that are then validated by a human.
What are the legal limitations of such automation?
In France, AI cannot make autonomous accounting decisions without human validation. The traceability of each decision is required by the Code de la comptabilité (French Accounting Code).
Book your free audit and discover how to automate your financial processes with DATALIA's AI: DATALIA →
