Data science🎓 AI+ Finance Practitioner™
AI & Finance
Understand how AI is transforming finance: credit, fraud, markets, blockchain and strategy.
- Duration
- 12 h · 2 days
- Level
- Beginner
- Format
- On site · Remote · Blended
- Max. group size
- 12 participants
Overview
This two-day program helps finance professionals understand how artificial intelligence is transforming their jobs. It covers AI fundamentals, data-driven decision making, credit granting, fraud detection, market forecasting, blockchain and emerging technologies. It ends with defining an AI adoption strategy, taking governance, ethics and change management into account. It is based on the AI+ Finance Practitioner framework.
Who this program is for
- Professionals in finance, banking and insurance
- Analysts, management controllers, risk managers and auditors
- Managers and executives involved in the digital transformation of the finance function
Prerequisites
- Basic understanding of finance
- Curiosity about the impact of AI on financial operations
- Openness to ethics and governance issues
- No technical skills required
Learning objectives
- Explain the key concepts of AI applied to finance
- Use data and AI to support financial decisions
- Describe AI's contribution to credit, scoring and loan processing
- Identify AI approaches for detecting fraud
- Understand the possibilities and limits of AI market forecasting
- Grasp the convergence of AI and blockchain
- Build a roadmap for adopting AI in the finance function
Detailed curriculum
8 modules · 12 h · 2 days
Day 1
Introduction to AI and its impact on finance
60% theory · 40% practice
- Fundamentals of AI applied to finance
- The role of AI in modern finance
- Benefits and challenges of adoption
- Overview: machine learning, predictive analytics, generative AI
Data-driven decision making
50% theory · 50% practice
- Sources and types of financial data
- Data quality and preparation
- Analyses, trends and actionable indicators
- AI-assisted decision making and financial strategy
🛠 Hands-on workshops
Turn data into a decision0.8 h
From a fictional dataset, identify trends and formulate a recommendation.
✅ Deliverable : A recommendation memo
Credit and loans: what AI changes
50% theory · 50% practice
- AI-based credit scoring and alternative data
- Predictive models of credit risk
- Automating loan processing and document handling
- Personalizing the offer and customer experience
🛠 Hands-on workshops
Assess a credit application0.8 h
Compare a traditional approach and an AI-assisted approach on a fictional case.
✅ Deliverable : A comparison grid
Fraud detection with AI
50% theory · 50% practice
- Overview of financial fraud
- Traditional approaches and their limits
- Anomaly detection, pattern recognition, real-time monitoring
- Outlook: adaptive detection
Day 2
Stock market forecasting
50% theory · 50% practice
- Fundamentals of market analysis
- Machine learning and predictive modeling
- AI-assisted trend analysis
- Volatility, data and model limits
Blockchain and AI
60% theory · 40% practice
- Blockchain fundamentals in finance
- Transparency and security
- AI and blockchain synergies: automation and data integrity
- Governance, ethics and responsible implementation
Emerging technologies and their impact on finance
60% theory · 40% practice
- The FinTech ecosystem and digital financial services
- Automation, digital platforms and next-generation technologies
- Integrating an emerging technology: adoption, business impact, innovation
Implementing an AI strategy in finance
30% theory · 70% practice
- Building a "digital-first" strategy
- Identifying and prioritizing use cases
- Operationalizing: integration, change management, governance
- Future trends and strategic resilience
🛠 Hands-on workshops
AI roadmap for the finance function1 h
In groups, define three priority use cases and a rollout plan.
✅ Deliverable : A roadmap presented to the group
Tools and technologies
ChatGPTGoogle SheetsMicrosoft ExcelPython
Certifications
AI+ Finance Practitioner™
An AI CERTs certification on AI applied to finance professions.
Teaching methods
- Presentations illustrated with real cases from the financial sector
- Case studies and group discussions
- Hands-on exercises on fictional financial data
- Workshop to build an AI roadmap
Assessment
- Quiz at the end of each module
- Final case study presented to the group
- Preparation for the certification exam
Required equipment
- A computer with an internet connection
- Access to a generative AI assistant (free account) for the exercises
Resources provided
- Course materials in French
- Fictional case studies and datasets
- Summary sheets for each module
Career outcomes
- A clear vision of AI uses in finance
- Ability to identify use cases in your organization
- Preparation for the AI+ Finance Practitioner certification