AI and Financial Performance: Automation, Reporting and Steering

AI is transforming financial performance by automating bank reconciliation, closing and reporting. Discover how CFOs and management controllers leverage BI and predictive analytics to steer differently.

By The DATALIA team

Quick answer: At a European fintech client of DATALIA, the integration of a sovereign AI into Odoo made it possible to reduce the monthly closing time from 9 days to 3 days, automate 94% of recurring accounting entries, and generate financial statements in real time. Steering moved from diffused Excel reporting to a single source of truth, with dashboards updated automatically and predictive alerts on budget variances. All of this without exposing sensitive data to external models.

1. Context and starting point
2. Issues and objectives
3. The solution implemented
4. Results achieved
5. What didn't work
6. Key takeaways
7. Frequently asked questions
8. Request a free audit

Context: A fast-growing European fintech

Client case: European fintech specialized in institutional payments, based in France and Belgium, with more than 200 employees and 35% organic growth per year.

The company manages over 50,000 accounting entries per month, spread across 12 different legal environments. Each month, the closing was handled by a team of 5 employees, who had to manually consolidate Excel files sent by the various entities.

Reporting was produced post-hoc, with delays of 5 to 7 days after closing. Budget variances were only detected during the monthly steering meeting, making any corrective action too late.

The tools from the Excel/ERP couple (Odoo Community) allowed data to be centralized, but not to be interpreted or anticipated. The lack of interoperability between the accounting, cash flow and reporting modules forced teams to multiply manual entries and cross-checks.

Issue: Gaining reactivity, accuracy and visibility

The objectives set at the outset were quantified:

• Reduce the duration of monthly closing from 9 to 3 working days;
• Automate at least 80% of recurring entries (expenses, depreciation, provisions);
• Produce corrected and unified financial statements within 24 hours after closing;
• Activate predictive alerts on budget variances starting at -5%;
• Ensure regulatory compliance without exposing sensitive data to the outside.

The finance department also wanted to move from retrospective reporting to forward-looking steering, incorporating forecast scenarios and dynamic cash flow indicators.

The solution implemented: Sovereign AI, intelligent reporting

DATALIA integrated a sovereign, private and self-hosted AI directly into the client's infrastructure, connected to the accounting and cash flow modules of Odoo and a centralized database (PostgreSQL). This architecture relies on a layer of type Retrieval-Augmented Generation (RAG), allowing the AI to analyze internal data without ever transmitting them to an external model.

1. Automation of accounting processes

The AI was trained to:

• Automatically classify accounting entries according to the general chart of accounts (GCO);
• Generate depreciation and provision entries according to defined rules;
• Detect duplicates and anomalies in entries in real time.

Business rules are versioned and auditable. When a particular case is encountered, the AI raises a flag and isolates it for a human.

2. Generative AI for reporting and writing

Once the data is consolidated, DATALIA.App uses generative AI to:

• Write monthly summary notes;
• Automatically create Power BI dashboards incorporating dynamic KPIs;
• Generate predictive cash flow reports based on recurring flows and historical trends.

The models are fed by internal data and not by generic parameters, ensuring contextual relevance.

3. Interactive dashboards and predictive alerts

Alerts are triggered as soon as an indicator deviates from the historical norm or from the budget forecast. For example:

• A variance of -5% on a cost center triggers an automatic notification to the manager;
• A slowdown in payment on a customer triggers an automated reminder email via the Odoo API;
• A "pessimistic" scenario simulation is generated each month to anticipate cash flow needs.

Results: A leap in productivity and accuracy

The following indicators were measured over a period of 6 months after deployment:

Finance teams have recovered more than 15 hours per month, reassigned to strategic analysis and regulatory monitoring. The CFO also noted a 72% improvement in the compliance rate of tax reports, thanks to automatic controls integrated into the AI.

What didn't work: The "all or nothing" approach

During the first phase, the team attempted to automate all accounting processes in a single wave. Result:

• The AI did not adapt well to the specific accounting of certain entities (e.g. intra-community VAT);
• Classification errors generated incorrect entries, requiring manual recovery;
• The lack of validation by employees led to partial rejection of the tool.

The fix was to segment the deployment by type of entry, train the AI on more targeted data batches, and activate a "semi-autonomous" mode for 3 months, where each automatic entry was reviewed before final validation.

Key takeaways

1. Deploy AI by type of process, not globally. Prioritize recurring entries (expenses, depreciation) before complex flows (VAT, international bank reconciliations).
2. Involve teams from the training phase. Their validation of the first batches allows adjusting business rules and ensuring buy-in.
3. Keep a human checkpoint for atypical cases. Automation should not become a black box.
4. Ensure data sovereignty. A local or hybrid AI avoids any exposure of sensitive financial data.
5. Plan for ongoing training. AI changes the role of the CFO, but does not make it obsolete.

Conclusion: AI at the service of strategic steering

The integration of a sovereign AI in the finance function goes beyond automation. It redefines the role of the CFO, moving from data processing to predictive and strategic steering.

With our client, the time saved is not just an efficiency gain. It allows the finance team to focus on data interpretation, risk anticipation, and building growth scenarios. Reporting becomes a living lever, and not a fixed deliverable.

The key to success lies in a gradual approach, clear governance, and data control. At DATALIA, we accompany every step of this transformation, from the initial audit to effective deployment, through team training. Our goal: to provide you with the tools to transform your data into informed decisions, without ever losing control.

Frequently asked questions

How does AI improve the reliability of financial statements?

AI automates the classification of entries, bank reconciliation, and coherence checks. This reduces human errors and allows correcting anomalies before closing. Business rules are auditable and atypical cases are subject to validation.

What types of automation are possible for a CFO?

Recurring entries (expenses, depreciation, provisions), bank reconciliation, Excel/PowerPoint report production, summary note generation, and budget alerts. More complex processes can be orchestrated via internal connectors.

Can AI replace management control?

AI executes repetitive tasks and provides insights, but management control remains the interpreter of data. It frees the CFO from execution to focus on analysis, steering and strategy.

Is it possible to use AI without exposing sensitive data?

Yes, by opting for a sovereign, private and self-hosted AI. DATALIA.App integrates into your infrastructure, with a RAG-type model that queries your local data without transmitting them outside.

How long does it take to deploy such a solution?

The timeline depends on the scope and complexity of processes. With our client, an initial operational phase was delivered in 8 weeks, with progressive deployment by module. An initial audit allows estimating a realistic schedule.

checklist : ready to scale up?

Before launching an AI project in finance, validate these criteria:

• Data is centralized and accessible via a reliable source (ERP, SQL database);
• The scope of automations is well defined and prioritized;
• Business rules are documented and validated by the concerned teams;
• A human checkpoint is planned for atypical cases;
• Data sovereignty is ensured (self-hosting or sovereign cloud);
• A training plan is integrated into the project, and an AI reference person is designated.

Discover DATALIA's sovereign AI designed for enterprise finance: DATALIA →