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Course Outline
Day 1: AI Fundamentals and AI-Enhanced Python for Finance
AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in finance.
- Exploring finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are suitable for AI assistance versus controlled automation.
Python for Finance – Utilizing AI as a Coding Partner
- Essential Python concepts for finance professionals, including variables, data types, conditions, functions, and notebooks.
- Collaborating with AI assistants to generate, explain, debug, and refine Python code, moving away from isolated coding practices.
- Applying prompting techniques to ensure reliable and finance-specific code generation.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Performing filtering, grouping, aggregation, and calculation of finance metrics.
- Leveraging AI to explain errors, optimize logic, and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Creating reusable Python workflows with AI-supported code reviews.
- Validating outputs prior to their use in finance reporting.
Practical Application
- Develop an AI-assisted Python workflow to analyse a sample finance dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardising finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date-related issues.
- Integrating data from multiple finance sources for comprehensive analysis.
Advanced Financial Analysis Techniques
- Analysing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period comparisons.
- Performing drill-down analyses to identify key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate movements, patterns, and unusual transactions.
- Generating analytical questions and hypotheses from finance data.
- Differentiating between useful signals and misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examining historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decisions.
- Using AI to support scenario narratives while maintaining financial controls.
Practical Application
- Execute end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted finance insight summary backed by underlying data.
Day 3: AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design Principles
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards focused on decision-making questions rather than visual complexity.
- Structuring views for executives, management, and analysts.
Creating Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Developing KPI cards, trends, variance visuals, drill-downs, and filters.
- Building views for budget vs. actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Utilizing natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations for KPI movements.
- Using AI to pinpoint areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation.
- Managing access to sensitive financial information and controlling distribution.
- Avoiding misleading visual or AI-generated conclusions.
Practical Application
- Construct an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial movements.
Day 4: Advanced AI Tools for General Ledger and Finance Operations
AI Applications in the General Ledger
- Analysing GL accounts, transaction patterns, and posting behaviour.
- Supporting transaction classification and account-level reviews using AI.
- Identifying unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Matching records and identifying exceptions across finance datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritising unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, and manual journals.
- Analysing period-end journals and generating supporting explanations.
- Establishing risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Prioritising close tasks and conducting exception-based reviews.
- Using AI for variance explanations, commentary, and review notes.
- Implementing structured approval and validation before final reporting.
Practical Application
- Analyse a sample GL dataset to identify anomalies and reconciliation exceptions.
- Produce a controlled, AI-assisted review summary for finance management.
Day 5: Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI in Finance
- Defining agentic AI workflows through goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI can support finance operations and where human approval remains critical.
- Comparing single-agent versus multi-step or multi-agent finance workflows.
Designing Agentic Finance Workflows
- Creating agents for data collection, analysis, validation, and reporting tasks.
- Connecting agents to structured finance data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implementing automated variance investigation and management commentary workflows.
- Managing GL exception triage, reconciliation support, and close-status monitoring.
- Refreshing forecasts, preparing scenarios, and deploying finance query assistants.
Governance, Risk, and Controls for Agentic AI
- Implementing human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Addressing data confidentiality, hallucination risks, validation, and model limitations.
- Defining safe operating boundaries prior to production deployment.
Final Practical Capstone
- Integrate Python with AI, advanced analytics, and dashboard outputs within a single finance use case.
- Design an agentic workflow that analyses results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps.
Requirements
- Fundamental knowledge of finance, accounting, financial reporting, or FP&A concepts.
- Experience with Excel and the ability to work with financial datasets.
- No prior Python programming experience is required, though basic familiarity with data analysis is advantageous.
- Basic awareness of AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is beneficial but not mandatory.
- Participants should be comfortable interpreting financial reports, KPIs, budgets, variances, and related finance data.
- A laptop equipped with access to required training tools, datasets, and approved AI platforms is necessary for practical sessions.
35 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 8000 € + VAT*
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