AI in Finance: Automating Financial Reporting for Finance Departments

Finance teams lose hours compiling data, reconciling journal entries, and verifying discrepancies. A sovereign AI connected to internal systems can reduce this time by 70% while ensuring full data traceability. Here's how one company automated its monthly financial reporting without relying on public tools.

A self-hosted AI connected to ERP and accounting systems enables finance departments to generate reliable financial reports in under 2 hours instead of 2 days, by automating data collection, reconciliation, and validation. Tested and validated in regulated environments.

Contents

1. Company context
2. Problem statement and objectives
3. The solution implemented
4. Results obtained
5. What didn't work
6. Key takeaways
7. Frequently asked questions

Company Context

A European fintech operating in cross-border payment processing, handling over 3 million transactions per month, encountered limitations in its financial reporting production. Its accounting team, made up of eight people, spent an average of 12 hours per month generating monthly financial statements for management and external partners.

The data came from five distinct sources: an internal ERP system, a customer accounting system, a treasury tool, a database of received payments, and a repository of operational expenses. Each source used a different format, and discrepancies between totals required hours of manual reconciliation.

Management sought to reduce report delivery delays, improve data accuracy, and ensure better traceability in line with European financial regulation requirements. The accounting team suffered from repetitive tasks, which reduced the time available for strategic analysis.

Problem Statement and Objectives

The objectives set by management were clear: reduce the time to produce monthly financial reports to under 3 hours, eliminate compilation errors due to manual entries, and ensure complete data traceability to facilitate internal and external audits.

Several major issues were identified. First, manually compiling data from five separate systems took between 6 and 12 hours per month depending on the period, with increasing risk of errors at month-end. Second, discrepancies between totals calculated by each source required in-depth investigation, often conducted late in the closing period, increasing stress on the team.

Finally, the lack of automated traceability made internal audits long and tedious. Each anomaly had to be traced manually, increasing the risk of non-compliance and reducing stakeholder confidence in the accuracy and reliability of the reports.

The Solution Implemented

The first step was to deploy a sovereign AI platform, hosted locally within the company's infrastructure, connected to the five existing systems via secure APIs. This platform, based on DATALIA.App, enabled real-time data collection, validation, and transformation, while maintaining full control over processing and storage.

Then, a data transformation layer was implemented to harmonize formats and currency units. Validation rules—such as admissible discrepancy thresholds or matches between accounting entries and bank transactions—were encoded as automated workflows. These workflows included consistency checks, alerts in case of anomalies, and semi-automated reconciliation processes for minor discrepancies.

Integration took place in three phases. Phase 1 connected the three most critical sources: the ERP, the customer accounting system, and the payments database. Phase 2 added the treasury tool and expense repository. Phase 3 enabled advanced features, such as automatic generation of interactive dashboards and scheduled report distribution to stakeholders.

A pilot was launched over a full month, with rigorous tracking of performance and user feedback. End users, composed of the accounting team and some managers, received customized training to maximize adoption of the new solution. Early iterations helped refine validation rules and further automate processes previously handled manually.

The testing phase included simulated error scenarios to assess system robustness. For example, intentionally removing an accounting entry verified that alerts were correctly generated and the reconciliation process worked as expected. These tests also identified edge cases not covered by the initial workflows, leading to quick improvements.

Results Obtained

The implementation of the sovereign AI solution transformed the way the company manages its financial reporting production. The average time needed to generate a monthly report dropped from 12 hours to 1 hour and 45 minutes—a reduction of over 85%. This improvement was driven by automated data collection, instant reconciliation of discrepancies, and direct generation of ready-to-use reports without manual intervention.

Beyond these time savings, report quality improved significantly. Compliance rate rose to 99.5% from 87%, thanks to rigorous automated validation. Data entry errors, once frequent, nearly disappeared. The accounting team freed up over 80% of its time to focus on strategic financial analysis, thereby increasing the value of its contributions.

Additionally, data traceability was strengthened. Every transaction is now linked to its original source, with a complete log of all processing performed. This transparency greatly facilitated internal audits, reducing audit time by 60%. Stakeholders—including external partners and regulators—expressed greater confidence in the accuracy and reliability of the financial reports produced.

What Didn't Work

The first version of the system failed to correctly integrate data from the treasury tool due to an incompatible export format. The team had to resort to a temporary solution involving manual file conversion, which reduced the overall efficiency of the solution for two months. This issue was resolved by developing a custom connector, but it highlighted the importance of thoroughly testing each integration before full deployment.

Another failure concerned the initial approach of automating the entire reconciliation process, without keeping a human role to validate significant discrepancies. This led to late detection of an exceptional discrepancy that could have been avoided with manual validation at a key step. The team subsequently reinstated a manual review process for anomalies exceeding a predefined threshold, combining automation and human expertise more effectively.

Finally, the initial user training was insufficient for some team members less comfortable with technology. Several operational questions arose, requiring additional training sessions. This experience showed that adopting an innovative solution goes beyond standardized training; it requires a personalized approach tailored to each user's skills and needs.

Key Takeaways

• Connect your existing data sources: seamless integration with your ERP and accounting tools is essential to avoid manual data entry.
• Start with a limited scope: automating a critical workflow, such as bank reconciliation, allows validation of the solution before broader deployment.
• Keep humans in the loop: atypical discrepancies require expert validation, even if most cases are automated.
• Plan for user training: successful AI adoption depends as much on technology as on team support and onboarding.
• Ensure traceability: every processed data point must be traceable to its source, in compliance with GDPR and accounting standards.

Practical Tips to Get Started

Identifying a repetitive and time-consuming process, such as journal compilation or bank reconciliation, is a good starting point to test the effectiveness of AI. Mapping all data sources involved—including their formats, export formats, and update frequencies—helps plan a realistic and targeted integration.

When selecting a solution, prioritize a self-hosted, GDPR-compliant platform to secure sensitive data. Evaluate available native connectors and the ability to create custom connectors based on the organization's specific needs. Conducting a quick proof of concept on one month of real data helps validate the solution before scaling up to more complex deployments.

Plan team training with interactive workshops, documentation, and personalized support. Schedule regular feedback sessions to collect user input and adjust the solution according to emerging needs. Gradually integrate AI into existing processes while maintaining manual controls for exceptional cases, ensuring a smooth and secure transition.

Frequently Asked Questions

Can AI replace a finance team?

No. AI automates data collection, validation, and compilation. Teams remain responsible for interpreting results, making strategic decisions, and handling unusual cases. Its role is to free up time for higher-value activities.

What data is covered by automation?

All accounting and financial data flowing between ERPs, customer accounting systems, treasury tools, and payment databases. AI can also incorporate external data, such as exchange rates or economic indicators, to enrich reports.

See how DATALIA can automate your financial processes

DATALIA is a digital transformation company combining consulting, custom solution integration, and training, with artificial intelligence at the core of its approach. DATALIA.App is a sovereign, private, and self-hosted AI platform within your environment, connected to your internal applications, compliant with GDPR and the AI Act.

Book your call and free audit today with a DATALIA expert: DATALIA →