Degree RNCP-Accredited, Level 7, 6 years of higher education
Entry Requirement: Bac+5
Duration: 12 months
Credits: 75 ECTS
Format: Work-study, initial, or continuing education
Language: French
Campus: Paris-Villejuif
Start date: October 2027

Program

From Data Mastery to AI Orchestration.

11 seminars, 4 major stages, 6 Finance AI Products, 2 certifications, the Boarding program, and the AI Clinic.

12 months, 350 hours, 75 ECTS, 6 Finance AI Products, 2 certifications

The Academic Pathway

Four major stages, from fundamentals to transformation.

Each seminar outlines its objective, key concepts, applications, and the associated project, if applicable.

01

Master & Scale

From financial data to an AI-ready infrastructure.

Before building with AI, you must master the raw material on which it relies: data. The first three seminars provide the fundamentals for understanding the technologies, leveraging financial data, and mastering the architectures that power AI solutions.

Objective: To understand how AI and data are already transforming financial roles, processes, and organizations.

Predictive AIGenerative AIAI AgentsAgent-Based SystemsData & CloudAutomationProcess Mapping

Overarching Question: Which processes, tasks, and decisions within the finance function can now be augmented or transformed by AI?

Objective: To transform financial data into reliable, visualizable, and actionable insights.

Data SourcesSQLPythonData PreparationStatisticsKPIsPower BIDashboardsFinancial Storytelling

Integrated Training: Microsoft Certified: Power BI Data Analyst Associate — PL-300

Finance AI Project #1 — Finance Data & Performance Cockpit: Build a financial management dashboard using data from multiple sources.

Objective: Understand how data flows through the organization and build an architecture capable of powering reliable analytics and AI solutions.

ERPCRMData WarehouseData LakeCloudAPIsETL/ELTpipelinesMaster DataData LineageData Quality

High-performing AI always starts with accessible, reliable, and properly structured data.

Finance AI Project #1 — In-Depth Look: Connecting Sources · Data Architecture · Quality · Traceability · Data Preparation.

 

02

Predict & Build

Moving from data analysis to creating AI solutions.

The augmented finance professional must do more than just interpret data. They must be able to anticipate trends, leverage AI models, and transform a use case into a full-fledged business project.

Objective: Learn how to use, compare, and interpret predictive models without becoming a data scientist.

regressionclassificationclusteringforecastingfeature engineeringMachine LearningDeep Learningexplainability

Applications: cash forecasting · budget forecasting · scoring · fraud · accounting anomalies · auditing · control · risk management.

Finance AI Project #2 — Predictive Finance Engine: Build, compare, and evaluate a predictive solution applied to a financial problem.

Objective: Master generative AI models and learn how to adapt them to the data, documents, and knowledge specific to the finance function.

LLMmultimodalPrompt EngineeringContext EngineeringStructured Outputsembeddingsvector basesRAGevaluationcosts

Applications: financial analysis · reporting · accounting documents · standards · document auditing · contracts · information retrieval · summaries.

Objective: To move from an idea or the simple use of a chatbot to a fully-fledged AI solution that can be deployed within the company.

Problem Framinguser needsspecificationsarchitectureAPIsUXprototypingtestingperformancecosts

Finance AI Project #3 — Generative AI Assistant for Finance: Design a specialized assistant capable of leveraging an organization’s data and knowledge to address issues in Finance, Accounting, Audit, or Internal Control.

03

Act, Orchestrate & Govern

When AI is no longer content to simply respond but begins to take action.

With AI agents, a new milestone has been reached. AI can access tools, interact with systems, perform tasks, and participate directly in financial processes. This opens up considerable potential but imposes new requirements for control, security, oversight, and accountability.

Objective: to transition from AI that responds to AI capable of acting in a controlled manner within a business process.

agentstoolsFunction CallingAPIsmemoryreasoningplanningpermissionshuman-in-the-loopcontrols

Applications: month-end closing · reconciliations · reporting · auditing · billing · collections · control · variance analysis · document collection.

Finance AI Project #4 — Financial AI Agent: Build an agent capable of executing all or part of a financial process in a controlled manner.

Objective: to transition from an isolated agent to an agent-based system integrated into the enterprise.

multi-agentorchestrationMCPA2Acoordinationobservabilityevaluationtracesfallbackhuman escalation

Integrated Preparation: Microsoft Certified: AI Agent Builder Associate

Finance AI Project #5 — Design: Agentic Finance Control Center: Design an agent-based financial system combining multiple agents, data, tools, and workflows within an orchestrated and monitorable environment.

Objective: Build AI systems capable of being used in a financial environment with high standards for trust, security, and accountability.

AI ActGDPRData GovernanceCybersecurityModel RiskLLM RiskAgent RiskBiasAuditabilityHuman Oversight

High-performance AI alone is not enough. It must be secure, governed, controlled, and auditable.

Finance AI Project #5 — Finalization: Transforming the agent-based system into a solution ready for supervision and governance—a true control center that allows us to know what the agents are doing, why, with what data, and when a human must take over.

04

Steer, Transform & Discern

Create value with AI and redesign the Finance function.

The final step is no longer about learning a new technology. It’s about knowing where to use it, how to deploy it, what value to expect from it, and how to reorganize business functions around it.

Objective: Transform an AI opportunity into a realistic, manageable, and value-creating project.

ScopingUse Case PrioritizationBuild/Buy/PartnerAgilePoCMVPBudgetTCOROIChange Management

Knowing how to invest in the right AI projects—but also knowing when to abandon those whose cost, risks, or complexity outweigh the value created.

Finance AI Project #6 — Launch: AI Finance Transformation Blueprint, applied to a real-world problem from the student’s professional environment.

Objective: Address both sides of the same transformation: the process and the professional’s role.

Process MiningTask DecompositionAutomation PotentialHuman/AI Allocationoperating modelsnew rolestarget architectureroadmap

As AI automates production and analysis, finance professionals create greater value through their judgment, decision-making ability, systemic vision, influence, and ethical responsibility.

Finance AI Project #6 — Finalization: Assessment of the Current State → AI Opportunities → Target Processes → Architecture → People & Agents → Costs → ROI → Risks → Governance → Roadmap.

Finance AI Product Portfolio

A degree certifies your skills. Your projects demonstrate them.

Throughout the 11 seminars, each student gradually builds a portfolio of 6 projects applied to Finance, Accounting, Auditing, and Internal Control. More than just a teaching method, it’s a true professional showcase.

Finance Data & Performance Cockpit

Build a financial dashboard using data from multiple sources.

Predictive Finance Engine

Build, compare, and evaluate a predictive solution applied to a financial problem.

Generative AI Assistant for Finance

Design a specialized assistant that leverages data and knowledge to address a problem in finance, accounting, auditing, or internal control.

Financial AI Agent

Build an agent capable of executing all or part of a financial process in a controlled manner.

Agentic Finance Control Center

Design and then oversee an agent-based financial system combining agents, data, tools, and orchestrated workflows.

AI Finance Transformation Blueprint

A transformation project based on a real-world challenge faced by the host company.

Learn by building. Build to transform.

 

Much more than a program

Four pillars that define the experience.

Finance AI Product Portfolio

A professional showcase: experiment with real-world business challenges, track your progress, have demonstrable achievements to highlight in interviews, and prove your dual expertise in Finance, Data, and AI.

Boarding for aivancity

A 20-hour online preparatory program (in collaboration with LinkedIn Learning), available upon admission: Python, SQL, data manipulation, APIs, algorithmic thinking. No prior programming experience is required for admission.

2 professional certifications

One Data certification, one AI certification:
Microsoft Certified: Power BI Data Analyst Associate — PL-300; Microsoft Certified: AI Agent Builder Associate. Recognized professional credentials that complement the MSc and the RNCP certification.

The AI Clinic

Work on real-world problems even before graduation: analyze a need, explore a use case, prototype a solution, test its relevance, and evaluate its value and risks. A space at the intersection of training, experimentation, and organizational transformation.

Here, AI isn’t just learned in the classroom. It’s put to the test in the real world.

A comprehensive program, from fundamentals to transformation.

The information presented on this page is provided for informational purposes only and is not contractually binding. Given the rapid evolution of Artificial Intelligence and Data, aivancity reserves the right to modify the program content, certifications, terms and conditions, schedule, and fees. The final terms and conditions are those communicated at the time of enrollment.