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Regional Governments & PNRR/ERDF Programs

Reporting, audit and monitoring on EU funds distributed across multiple implementing bodies demand full traceability, not just speed. Alongside document management sits the need to evaluate ex ante how policies will affect local households and businesses.

Problems

  • Program documentation scattered across offices, implementing bodies and different archives
  • Audit and reporting requirements toward national and European oversight bodies
  • Long turnaround times collecting and verifying data from third parties
  • Need to quickly reconstruct the progress status of measures and calls for tender
  • Static, linear macro and micro multipliers that miss the redistributive and financial effects of policies at the level of individual households or firms

Relevant systems

  • Enterprise Search across calls, measures and PNRR/ERDF circulars
  • Document Intelligence for reporting documentation
  • Inter-agency knowledge management with sources always cited
  • AI Security & Guardrails for the audit trail required on EU funds
  • Population and business digital twins with micro-simulation models to evaluate public-policy effects ex ante

KPIs

  • Time to prepare reporting documentation
  • Reduction in errors found during audits
  • Response time to oversight-body requests
  • Share of documentation actually monitored
  • Accuracy and turnaround time of policy-impact simulations

Related case study

Regional GovernmentsIllustrative scenario

Population and business digital twin for public-policy evaluation

A regional government needs to evaluate ex ante the effects of tax, welfare or macroeconomic-shock response measures on local households and businesses, but only has static, linear macro and micro multipliers: aggregate estimates that miss redistributive, employment and financial-risk effects at the level of individual households or firms.

Analysis

Inventory of the region’s data assets (registries, tax data, business-registry records, cyclical and demographic indicators), assessment of available computing capacity, and definition of the priority policy questions to simulate, from a multidimensional view of profitability, financial soundness and well-being.

Solution

Building a population and business digital twin through dedicated micro-simulation models that replicate the region's households and firms, to simulate ex ante the effects of tax-benefit measures and macroeconomic shocks, with a multi-year time horizon and breakdowns by geographic area and sector.

Architecture

Micro-simulation models for households and businesses, integration with regional data lakes and statistical registries, high-performance computing infrastructure for running scenarios, summary dashboards for decision-makers, and versioning/audit of simulated scenarios.

Implementation

Rollout on an initial set of regional policies, validation of results with the decision-makers involved, calibration of the models on historical data, and subsequent extension to other measures, sectors and areas of the region.

Expected results

  • Ex-ante comparison of alternative policy scenarios before implementation
  • Disaggregated view of redistributive, employment and financial-risk effects
  • Greater transparency in evaluating the impact of public spending
  • Shared analytical basis for technical offices and policy-makers

ROI: Value is measured in policies better calibrated to their objectives, lower risk of measures with unintended effects, and reduced time needed to evaluate alternative scenarios before deciding.

Public reference: Amartya, the population and business digital twin developed by the Emilia-Romagna region with the Universities of Bologna and Modena-Reggio Emilia to simulate the effects of tax and welfare policies. This is not a project delivered by Futura AI: we cite it as a public example of this type of approach. margherita.regione.emilia-romagna.it/it/amartya

Reference architecture

Authorized sources
Classification, OCR, RAG
Guardrails
Operator
Business system
Audit trail
See our design method

Compliance and governance

Data governance

Classification of the data processed, minimization, no training on client data without explicit authorization, segregation between environments and clients.

Audit trail

Every source consulted, action taken and assisted decision is logged in a verifiable way, with reference to the originating data.

Human-in-the-loop

High-impact actions remain subject to explicit human confirmation; the system flags uncertain cases instead of deciding on behalf of people.

AI Act

Risk classification of the system, technical documentation and transparency requirements aligned with the European regulatory framework, handled from the design phase onward.

See the full security and governance model

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