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

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

When Marie, Finance Director of a French industrial group with 450 employees spread across six sites, received the report for the March closing, she noted what many finance departments know all too well: 72 hours of manual work, bank reconciliation errors, and a three-week delay before operational managers had access to their key indicators.

“We spent more time checking discrepancies between our systems than analyzing real variances,” 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 up for strategic analysis.

This article shows how a structure similar to yours can move from reactive management to proactive financial oversight 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 input, bank reconciliations, data extraction for reporting tools, and consistency checks between different information systems.

In the case of our industrial client, this figure rose to 45% due to data fragmentation: Odoo ERP for accounting, Salesforce CRM for sales, a specific HR tool for payroll, and Excel spreadsheets for forecasting. Each month, six full-time equivalents were dedicated to manual transfers, cross-referencing, and validation of inconsistencies.

While essential, these activities added no value for management. They also carried risks: input errors, undetected duplicates, and missed invoices. According to the CNCC (French National Accounting Council), 17% of accounting disputes between companies and tax authorities stem from errors in manual processing.

The 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 project begins with a thorough audit phase called Vision & Analysis (VASPIS), designed to identify pain 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 flows;
• Manually produced reports without audit trail;
• Inability to quickly generate forecast 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 off-the-shelf solutions, no financial data ever leaves your environment. This approach meets GDPR and AI Act requirements while ensuring full transparency over data processing.

For this client, the AI was connected to the following data streams:

• Journal entries from Odoo;
• Sales data extracted from Salesforce;
• Payslips from the HR system;
• Bank statements in PDF, automatically analyzed.

The AI learns input patterns, corrects anomalies, and feeds a unified data repository accessible through a conversational interface. For example, a finance department can ask: “What is the cash flow forecast for the next quarter?” and receive a quantified answer, along with data sources and alternative scenarios.

Odoo ERP: the solid foundation for automation

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

• Automatic bank reconciliation rules;
• Pre-approved validation workflows based on budget thresholds;
• Dynamically updated reports in real time;
• Automated alerts for budget overruns or payment delays.

Thanks to the Odoo API, data is continuously synchronized with DATALIA.App, which enriches analysis with predictive indicators.

Automating 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 verification with the sales CRM by teams;
4. Compilation of reports in a shared Word document;
5. Sending to regional managers for validation.

This time-consuming and error-prone process averaged 72 hours. With the DATALIA solution:

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

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

Before/after report: key indicators

Measurement period: six months post-deployment, compared to six months prior to intervention. Efficiency gains translate not only to reduced processing time but also to 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 a single phase. Result: overload, delays, and loss of team confidence. Correction: prioritize monthly closing, then gradually extend to payroll and forecasting.

2. Neglecting historical data. AI models were deployed without thoroughly cleaning 18 months of historical accounting data. Anomalies kept appearing. Correction: collaborative cleanup phase with finance teams, validated through sampling.

3. Lack of operational training. Although the conversational interface is simple to use, end users did not know how to ask the right questions to the AI. Correction: targeted workshops focused on use case scenarios, with reusable prompt templates.

“These failures weren’t catastrophic, but they taught us to move slowly at the beginning in order 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 lessons learned from this experience:

Start small, scale up in complexity. Automate monthly closing first, then payroll, then forecasting. Each step must be validated by teams before moving to the next.

Data quality above all. AI cannot correct fundamentally inconsistent data. A source audit and initial cleanup are mandatory.

Involve users from the start. Finance teams must participate in defining automated workflows. They are the best guardians of rule relevance.

Choose a sovereign solution. In an increasingly strict data protection context, locally hosted AI eliminates leak risks and ensures GDPR compliance.

Plan a training program. Even the most user-friendly solution requires human investment. Practical workshops help embed best practices.

Scaling up: the predictive dimension

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

• Predictive analysis of customer risk, combining payment history, CRM behavior, and industry indicators;
• Simulation of tax impact for merger or acquisition scenarios;
• Automatic detection of budget anomalies during the month, with alerts sent to project managers.

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

Key results summary

Monthly closing reduced from 72 hours to 24 hours; Error rate divided by six, from 6% to below 1%;

Released six full-time equivalents for strategic analysis;

Estimated annual savings of €180,000;

Real-time access to key indicators via a sovereign conversational AI.

For any finance department facing similar challenges, DATALIA offers a free process audit followed by a customized demonstration. Discover how sovereign AI can transform your financial oversight.

Can AI really replace manual accounting tasks?

Yes, for standardized processes like data entry, bank reconciliation, and report generation. AI excels at pattern recognition and correction of repetitive anomalies. Complex decisions—amortization, provisions, valuations—remain human, supported by analysis and recommendations provided by the AI.

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 occurs locally with complete audit logs. This meets GDPR and AI Act requirements while maintaining full traceability.

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