AI Finance: Automation and Steering for Financial Performance

Discover how AI finance drives financial performance through automation, intelligent reporting, and data-based decision making.

The DATALIA team · Published on May 12, 2025 · Updated on May 12, 2025

• Context
• Problem and objectives
• The solution implemented
• Results
• What didn't work
• Key takeaways
• Frequently asked questions

Context

DATALIA supported a European fintech operating in equity crowdfunding. This company, located in France and Belgium, was managing an increasing volume of customer files with a seven-person financial planning and analysis team.

The company was using scattered tools: one Excel spreadsheet for monthly consolidation, one CRM for customer relations, and one internal management system for cost accounting. Each monthly closing required three weeks of manual work, with an estimated data entry error rate of 4% according to an internal audit.

The objective was to implement AI finance to automate repetitive tasks, improve the reliability of reports and free up time for strategic analysis.

Problem and objectives

The objectives set at the beginning of the project were measurable and clear:

• Reduce the monthly closing period from 21 days to 7 days.
• Decrease the data entry error rate from 4% to below 1%.
• Automate 80% of recurring reports (cash flow, margin per product, KPI indicators).
• Produce real-time dashboards accessible to decision makers.

These targets were validated by the finance department and the CFO before the launch.

The solution implemented

DATALIA deployed a four-phase approach, integrating artificial intelligence at the heart of financial management:

Phase 1 — Mapping of flows and identification of automatable tasks

A multidisciplinary team listed 137 recurring tasks related to reporting, consolidation and financial analysis. Among them, 92 were identified as automatable through AI finance. These tasks included automatic bank reconciliation, classification of accounting entries, and generation of monthly charts.

A centralized data model was built by integrating:

• the accounting flows in real time from the management system,
• the customer data from the CRM,
• the budget forecasts entered by project managers.

An operational deliverable was used to validate the mapping:

Objective: Identify automatable tasks in the reporting process
To gather: Accounting processes, tools used, report frequency
Method:
- List all recurring tasks over 30 days
- Classify each task based on its automation level (manual, semi-automated, automatable)
- Prioritize high value-added non-automatable tasks
Output: A prioritized matrix with the % of estimated time savings per task
Annotation: This model works when data is normalized. It fails on processes still handled in paper mode.

Phase 2 — Deployment of AI assistant and targeted automation

DATALIA.App, DATALIA's sovereign and self-hosted AI, was configured to:

• automatically generate standard Excel and PowerPoint reports,
• query internal databases using natural language,
• send predictive alerts in case of significant budget variance.

The AI was connected to the internal APIs of the CRM, accounting system and forecasting engine. All data remains hosted on the fintech's internal network, without any third-party transfer.

Phase 3 — Implementation of dynamic dashboards

Interactive dashboards were created with key indicators:

• net cash flow per product,
• gross margin per distribution channel,
• client risk/credit ratio,
• actual forecast variance.

These indicators are updated daily through automated flows, allowing managers to react quickly.

Phase 4 — Training and governance of AI finance

A two-day training session was provided to the financial planning and analysis team. An AI referent was appointed to validate each new report generated by the assistant. A usage charter was established, including:

• the source data validation rules,
• the automatic alert thresholds,
• the review process for predictions.

Results

The results obtained six months after deployment are presented below:

Measurement period: from January to June 2025. The data comes from the internal system and usage logs of DATALIA.App.

What didn't work

The deployment had two noticeable failures:

• The predictive model for customer payment defaults showed 62% accuracy against the 78% expected. The reason: historical data was incomplete for 12% of old files. The model was recalibrated after data enrichment.
• The first version of the cash flow dashboard had 24-hour refresh delays. A bug in the CRM API was fixed in collaboration with the vendor within three weeks.

Key takeaways

• AI finance does not replace the financial planner, but amplifies its strategic impact.
• The quality of source data is critical: an AI model cannot compensate for biased or incomplete data.
• Self-hosting and data sovereignty facilitate internal adoption, especially in regulated structures.
• Post-deployment governance is as crucial as the technical part: an AI referent and a usage charter avoid misinterpretations.
• Gains are exponential when AI is connected to several internal systems (CRM, accounting, forecasting).

Objective: Measure the impact of financial automation on your department
To gather: Time spent on manual tasks, report frequency, errors observed
Method:
- Identify hours spent per type of task over 1 month
- Calculate the average hourly cost of the team
- Estimate the % of automatable tasks
- Calculate the potential annual savings
Output: A report with the estimated ROI and automation priorities
Annotation: This model is valid starting from 5 recurring users. It requires reliable time tracking.

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Frequently asked questions

Is AI finance GDPR compliant?

Yes, when deployed as a sovereign and self-hosted solution, like DATALIA.App. This approach ensures that sensitive data never leaves the company's infrastructure, which simplifies compliance.

How much does an AI deployment for finance cost?

Costs vary depending on the size of the organization and the complexity of processes. A free audit allows to size the scope and the potential return on investment. DATALIA offers this first analysis service.

In short

• AI finance allows to divide the closing period by 3 and free up to 85 hours/month for strategic analysis.
• Automation requires strong governance and high-quality data to generate reliable results.
• DATALIA supports CFOs and financial planners in integrating sovereign and custom-built solutions.

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