AI in finance: how a finance department automated its monthly closing

A finance department of 450 employees reduced monthly closings from 7 days to 24 hours using AI in finance and accounting process automation.

When Marie, financial director of a French industrial group with 450 employees spread across six sites, received the March closing report, she found what many finance departments know: 72 hours of manual work, banking reconciliation errors, and a delay of three weeks before operational managers had their key indicators.

« We used to spend more time checking discrepancies between our systems than analyzing real discrepancies », she recalls. That month marked a turning point: the following month, with the help of a finance AI solution integrated by DATALIA, the closing was completed in 24 hours, freeing teams for strategic analysis.

This article outlines how a structure similar to yours can move from reactive management to proactive financial management through automation, business intelligence, and data-driven decision-making.

The hidden cost of manual processes in finance departments

Studies show that accounting teams spend on average 30% of their time on repetitive tasks: journal entry posting, bank reconciliations, data extraction into reporting tools, or consistency checks between different information systems.

In the case of our industrial client, this figure rose to 45% due to a multiplicity of sources: Odoo ERP for accounting, Salesforce CRM for sales, a specific HR tool for payroll, and Excel spreadsheets for forecasts. Each month, six full-time equivalents were devoted to manual transfers, cross-referencing, and inconsistency validations.

These activities, while essential, created no value for management. They also introduced risks: data entry errors, undetected duplicates, or forgotten supplier invoices. According to CNCC (French Accounting Standards Board), 17% of accounting disputes between companies and administrations stem from errors in manual processes.

Lack of real-time visibility was equally problematic. Key financial indicators (cash flow, gross margin, debt ratio) were only available after a week of consolidation, while decision-makers needed this data to adjust forecasts or validate investments.

The DATALIA approach: from audit to implementation

At DATALIA, every intervention begins with a thorough audit phase, called Vision & Analyse (VASPIS), identifying friction points and underlying data sources.

For this industrial client, the audit revealed five critical areas:

• No automatic integration between Odoo and the CRM ;
• Use of Excel as a consolidation tool, with version conflict risks ;
• No real-time tracking of cash flow ;
• Manually produced reporting deliverables, without traceability ;
• Inability to quickly generate forecasting scenarios.

The implemented solution combines two components:

DATALIA.App: sovereign AI at the service of finance

DATALIA.App is a sovereign, private, and self-hosted AI, designed to integrate directly into your infrastructure. Unlike a public solution, no financial data ever leaves your environment. This approach meets GDPR and AI Act requirements while offering full transparency on information processing.

For this client, the AI was connected to the following flows:

• Accounting entries from Odoo ;
• Sales data extracted from Salesforce ;
• Payslips from the HR system ;
• Bank statements (PDF) analyzed automatically.

The AI learns entry patterns, corrects anomalies, and feeds a unified data repository, accessible via a conversational interface. For example, a finance department can ask: « What is the projected cash flow for next quarter? » and receive a numerical answer, data sources, and alternative scenarios.

Odoo ERP: a solid foundation for automation

The Odoo ERP, integrated by DATALIA, serves as the transactional backbone. All accounting entries, supplier invoices, customer payments, and budget allocations are centralized. DATALIA customized Odoo to include:

• Automatic bank reconciliation rules ;
• Pre-approved validation flows according to budget thresholds ;
• Dynamically updated real-time reports ;
• Automatic alerts on budget overruns or payment delays.

Through Odoo's API, data is continuously synchronized with DATALIA.App, which enhances analysis with predictive indicators.

Automation of monthly closings

Before the intervention, the monthly closing followed a linear but heavy process:

1. Manual extraction of entries from Odoo ;
2. Bank reconciliation done file by file in Excel ;
3. Discrepancy checking with the CRM sales team ;
4. Report compilation in a shared Word document ;
5. Sending to regional managers for approval.

This time-consuming and error-prone process took an average of 72 hours. With the DATALIA solution:

1. DATALIA.App automatically extracts entries from Odoo via the API ;
2. The AI compares these entries with bank statement PDFs, correcting unrecognized movements using a continuous learning model ;
3. Discrepancies are cross-checked with Salesforce data to identify expected receipts ;
4. A consolidated report is automatically generated, including residual discrepancies and adjustment recommendations ;
5. Regional managers receive an executive summary with priority alerts.

Result: closing completed in 24 hours, with an error rate below 1%, compared to 6% previously. The six full-time equivalents freed were reassigned to budget analysis and forecasting scenario development.

Before/after report: key metrics

Measurement period: six months post-deployment, compared to six months before the intervention. Efficiency gains are not only reflected in reduced processing time but also in faster response to market fluctuations.

What didn’t work: mistakes to avoid

The first version of the integration failed due to three common mistakes:

1. Too broad a scope from the start. The client wanted to automate all accounting processes in one phase. Result: overload, delays, and loss of team confidence. Correction: prioritize monthly closing, then gradually extend to payroll and forecasting.

2. Neglect of historical data. AI models were deployed without thorough cleaning of 18 months of historical accounting data. Inconsistencies kept arising. Correction: collaborative cleanup phase with finance teams, validated by sampling.

3. Lack of operational training. While the conversational interface is easy to use, end-users did not know how to formulate the right questions to the AI. Correction: targeted training workshops on usage scenarios, with reusable prompt templates.

« These failures were not dramatic, but they taught us to move slowly at the start to move faster later », explains Marie. « AI is not a magic solution. It requires data preparation and team training. »

Key takeaways for other finance departments

For any finance department considering such a project, here are the lessons learned from this experience:

• Start small, increase complexity. First automate monthly closing, then payroll, then forecasting. Each step must be validated by teams before moving to the next.
• Data quality first. AI cannot fix fundamentally inconsistent data. A source audit and initial cleanup are essential.
• Involve users from the start. Accounting teams must participate in defining automated flows. They are the best guardians of rule relevance.
• Choose a sovereign solution. In an increasingly strict data protection environment, locally hosted AI eliminates leakage risks and ensures GDPR compliance.
• Plan a training program. Even the most intuitive solution requires human investment. Practical workshops help embed best practices.

Scaling up: the predictive dimension

A year after the initial deployment, the group extended the use of DATALIA.App to other areas:

• Predictive analysis of customer risks, combining payment history, CRM behavior, and sector indicators ;
• Tax impact simulation for acquisition or disposal scenarios ;
• Automatic detection of budget anomalies mid-month, with alerts sent to project managers.

« Today, I no longer manage the finance department, I pilot it », sums up Marie. « AI handles repetitive tasks, and I can focus on what matters: supporting management in decision-making. »

To remember: summary of results

• Monthly closing reduced from 72 hours to 24 hours ;
• Error rate divided by six, from 6% to less than 1% ;
• Freed six full-time equivalents for strategic analysis ;
• Estimated annual savings of 180,000 € ;
• Real-time availability of key indicators via a sovereign conversational AI.

For any finance department facing similar challenges, DATALIA offers a free audit of your processes, followed by a personalized demo. Discover how a sovereign AI can transform your financial management.

Frequently Asked Questions

Can AI really replace manual accounting tasks?

Yes, for standardized processes such as data entry, bank reconciliation, or report generation. AI excels at pattern recognition and correction of recurring anomalies. However, complex decisions (amortizations, provisions, valuations) remain human, supported by analyses and recommendations.

How to ensure GDPR compliance with an AI solution in finance?

By choosing a self-hosted solution like DATALIA.App, no sensitive data leaves your infrastructure. All processing is performed locally with a complete audit trail. This meets GDPR and AI Act requirements while maintaining full traceability.

Automate your finance department with DATALIA's sovereign AI: DATALIA →