AI Finance: Automation and Performance Management

A sovereign AI connected to accounting and financial data enables the automation of reconciliation, consolidation, and monthly closing, while keeping control of data flows on your infrastructure.

Quick answer: At DATALIA, we deployed DATALIA.App, a sovereign and self-hosted AI, at a European fintech to automate multichannel centralization and real-time analysis of customer feedback. Result: a 72% reduction in time spent on monthly closing and an 85% improvement in cash reporting accuracy. The AI is hosted locally, compliant with GDPR and the AI Act, with no sensitive data exchanged with third parties.

Context: A European Fintech Facing the Multiplication of Financial Data Flows

The company studied, a European fintech specializing in payments and consumer credit, manages over 2 million transactions per month across three main channels: mobile, web, and integrated partners. Each channel generates heterogeneous CSV, JSON, or XML files, with variable date formats, currencies, and fields. The finance team, composed of 12 people (CFO, financial controllers, analysts), spent on average 9 working days per month collecting, verifying, and manually consolidating these flows.

The process, until then considered “acceptably slow,” became critical when the AI Act came into scope. Customer documents containing banking and personal financial data were passing through email inboxes and uncontrolled shared storage spaces. The DPO quickly identified a major risk of data leak, especially since some employees were using public AI assistants (like ChatGPT) to “synthesize” cash reports containing sensitive figures.

Alerted by the DPO, the CIO launched a call for projects: find an AI solution capable of automating the centralization, normalization, and analysis of financial flows, without transmitting the data outside the existing infrastructure. The specification also required the solution to be able to automatically generate management dashboards (cash flow, credit risk, performance by channel) and to issue predictive alerts in case of anomalies or concerning trends.

Measurable Objectives Set at Launch

• Reduce monthly closing time from 9 days to 2.5 days within 6 months.
• Improve cash reporting accuracy from 78% to 95% (measured by post-closing reconciliation).
• Eliminate the use of public AI solutions for handling sensitive financial data.
• Automatically generate 80% of monthly management dashboards.
• Detect and report 95% of flow or customer behavior anomalies.

Problem Statement and Objectives: Between Regulatory Requirements and Operational Pressure

The fintech, while innovative in its products, suffered from technical and organizational debt in its financial management processes. Teams used Excel as their main tool, with hundreds of complex formulas and outdated macros. The slightest error in format or currency could lead to hours of correction. The lack of automation made monthly reports not only long to produce but also prone to divergent interpretations between departments.

In parallel, the gradual entry into force of the AI Act (European regulation on artificial intelligence) imposed an urgent review of practices. AI systems used in decision-making or scoring processes had to comply with strict requirements in terms of transparency, traceability, and bias. However, the informal use of ChatGPT by employees, even for seemingly minor tasks such as data synthesis, exposed the company to non-compliance risks and data breaches.

The initial challenge was therefore threefold: automate a complex and sensitive process, while complying with strict regulatory constraints, without recourse to black-box tools. The choice of a sovereign, self-hosted AI solution became not only a technical opportunity but a strategic necessity.

The Solution Implemented: DATALIA.App, a Sovereign AI for Finance

DATALIA was selected for its sovereign approach and experience in deploying self-hosted AI in regulated environments. The proposed solution, DATALIA.App, is a sovereign, private, and self-hosted AI platform, designed to be connected to the company's internal applications. Unlike public AI assistants, DATALIA.App never transmits data outside the client's infrastructure. It is hosted on the fintech's servers, managed by its IT team, and compliant with GDPR and the AI Act.

The implementation took place in four phases:

1. Integration and connectivity: Connecting DATALIA.App to internal APIs (CRM, payment system, customer database) and file flows (SFTP, network shares). Configuration of secure ingestion channels.
2. Flow modeling: Creating automated pipelines for normalization, validation, and consolidation of data from the three channels. Implementation of business rules to harmonize formats and detect anomalies.
3. Analysis and reporting: Deploying predictive models for cash flow forecasting, fraud detection, and profitability analysis by channel. Automatic generation of reports and interactive dashboards.
4. Security and compliance: Enabling complete logging of all AI actions, configuring access controls, and establishing decision traceability. Internal audit of GDPR/AI Act compliance.

Each phase was accompanied by targeted training for finance and IT teams, as well as regular reviews with the DPO to ensure compliance with regulatory requirements.

Chronological Steps and Key Technical Decisions

The first critical decision was to reject any public cloud solution. Alternatives such as ChatGPT Enterprise or Microsoft Copilot, while attractive in terms of functionality, required data transfer to third-party infrastructures. Even if encrypted, these transfers were deemed unacceptable by the DPO, given the sensitive nature of the data (banking data, payment histories, customer profiles).

The second decision concerned the architecture of DATALIA.App. After evaluation, the team opted for a hybrid deployment: the AI model is hosted on-site, while software updates are managed by DATALIA via an encrypted channel. This allows maintaining full control over data while benefiting from continuous model improvements.

Finally, the third decision concerned governance. A monthly steering committee, composed of the CFO, CIO, DPO, and a representative from the DATALIA team, was established to validate developments, supervise alerts, and ensure alignment with business objectives.

Results: Before/After Table with Measurable KPIs

The results, measured over a six-month period (May to November 2024), show a significant improvement in operational efficiency and data accuracy. The time spent on monthly closing was reduced by 72%, allowing the finance team to focus on strategic analysis rather than manual data entry. Cash reporting accuracy increased from 78% to 95%, eliminating costly discrepancies and inter-departmental back-and-forth.

In parallel, the use of public AI assistants completely disappeared. The DPO was able to close the alert regarding shadow AI, and the company-wide rollout of DATALIA.App now includes strict policies for handling sensitive data. Finally, the equivalent operational cost (salaries + licenses + maintenance) decreased by 62%, offering a fast and measurable return on investment.

What Didn’t Work: Lessons Learned from Limitations and Failures

The first difficulty encountered was resistance to change from some teams. Several financial controllers, accustomed to their mastered Excel sheets and macros, initially struggled to trust an automated system. Their main concern was losing granularity and control over the data. This resistance was overcome through extended training and the provision of a “manual override” mode for exceptional adjustments.

A second failure concerned the initial complexity of data consolidation. The three channels (mobile, web, partners) did not always share the same customer identifiers or timestamps. The system took several weeks to learn how to link these entities, generating duplicates and inconsistencies during the calibration phase. This required the intervention of a data engineer to harmonize sources before full automation could take place.

Finally, trend prediction initially suffered from a lack of sufficient historical data to train the predictive models. Cash flow forecasts, while technically correct, were sometimes far from reality. It took three additional months of data before model accuracy reached the required level (95%).

Limitations of the Approach: What the AI Does Not Solve

• The AI cannot impute missing or incorrect values in source systems. Data quality at input remains crucial.
• Predictive models are sensitive to structural market changes (new regulations, economic crises) and require frequent retraining.
• Automation does not replace critical human analysis. Strategic decisions still rely on team expertise.
• The solution requires a sufficiently robust local IT infrastructure to host the model, which can be a barrier for small organizations.

Key Takeaways: Reproducibility and Conditions for Success

• Start with a thorough audit: Map all financial data flows and identify friction points before choosing an AI solution.
• Involve the DPO from the start: Compliance cannot be an afterthought. It must guide every technical decision.
• Choose a sovereign AI: In the context of sensitive data, local hosting and access control are non-negotiable.
• Plan a gradual skill-building process: Adoption succeeds when teams understand the tool and derive immediate value from it.
• Implement continuous governance: A regular steering committee ensures business-IT alignment and correction of deviations.

Conclusion: Sovereign AI, a Driver for Sustainable Financial Performance

This case study demonstrates that a sovereign AI, properly integrated into existing infrastructure and guided by rigorous governance, can radically transform the performance of finance teams. By automating repetitive tasks and providing real-time visibility into flows, DATALIA.App enabled the fintech to improve responsiveness, accuracy, and compliance, while reducing operational costs.

For hesitant CFOs and financial controllers, the lesson is clear: the challenge is no longer choosing between performance and security, but reconciling them. An AI that responds well but whose data path you cannot trace is not deployable. In contrast, an AI hosted locally, connected to your systems, and auditable at every step becomes a strategic partner, not just a tool.

In summary

• DATALIA.App is a sovereign, private, and self-hosted AI within your environment, connected to your internal applications, compliant with GDPR and the AI Act.
• Poorly framed automation costs more than a missing ERP.
• Compliance is not a barrier to deployment; it is its condition of survival.
• Nine out of ten files require no decision. It’s the tenth that justifies your team.

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Frequently Asked Questions

Is a sovereign AI truly more powerful than a public assistant?

In cases where latency or answer accuracy are critical, yes. But the real difference is traceability: every decision is recorded, every access is controlled, and no data is exposed to a third party. Raw performance is often comparable, but trust and compliance prevail.

What is the cost of a self-hosted AI deployment for a finance team of 10 people?

It varies depending on the existing infrastructure and the flows to automate. In the case studied, ROI was achieved in 4 months, thanks to reduced closing time and technical debt. A free audit can estimate the real cost of your context.

Source: Deployment carried out by DATALIA at a European fintech, between April and September 2024. The figures presented are measured results over a six-month period, using a standardized calculation method (moving average of monthly closings).