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Duration 35 hours
Course Outline
AI Fundamentals and Python with AI for Finance
AI, Analytics and Agentic AI in Modern Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in finance.
- Examining finance use cases spanning accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are best suited for AI assistance versus controlled automation.
Python for Finance - Using AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
- Utilising AI assistants to generate, explain, debug, and refine Python code, moving away from isolated coding practices.
- Employing prompting techniques to ensure reliable, finance-focused code generation.
Working with Financial Data in Python
- Importing Excel and CSV data via Pandas and DataFrames.
- Executing filtering, grouping, aggregation, and calculation of finance metrics.
- Using AI to clarify errors, optimise logic, and document analytical steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code review.
- Verifying outputs prior to their application in finance reporting.
Hands-on Application
- Constructing an AI-assisted Python workflow to analyse a sample finance dataset.
- Reviewing generated code, testing assumptions, and refining outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality
- Cleaning, validating, and standardising finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Integrating data from multiple finance sources for comprehensive analysis.
Advanced Financial Analysis
- Analysing revenue, costs, margins, profitability, and working capital.
- Conducting budget-versus-actual, variance, and period-over-period comparisons.
- Performing drill-down analyses to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Leveraging AI to investigate movements, patterns, and irregular transactions.
- Formulating analytical questions and hypotheses from finance data.
- Distinguishing relevant signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Evaluating historical trends, drivers, and forecasting assumptions.
- Conducting what-if and sensitivity analyses to support finance decisions.
- Using AI to enhance scenario narratives while maintaining financial controls.
Hands-on Application
- Executing end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Preparing a concise, AI-assisted finance insight summary backed by underlying data.
AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting relevant KPIs for finance, management, and operational reporting.
- Designing dashboards centred on decision-making questions rather than visual volume.
- Structuring views for executives, management, and analysts.
Building Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard utilisation.
- Creating KPI cards, trends, variance visuals, drill-downs, and filters.
- Developing views for budget-versus-actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Exploring financial data through natural-language querying.
- Generating AI-assisted summaries and explanations of KPI fluctuations.
- Using AI to highlight areas requiring in-depth analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation.
- Managing access to sensitive financial information and controlling distribution.
- Preventing misleading visual or AI-generated conclusions.
Hands-on Application
- Constructing an interactive financial dashboard using a structured dataset.
- Incorporating AI-supported management commentary linked to measurable financial changes.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analysing GL accounts, transaction patterns, and posting behaviour.
- Utilising AI to support transaction classification and account-level reviews.
- Spotting 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 to assist with variance explanations, commentary, and review notes.
- Implementing structured approval and validation before final reporting.
Hands-on Application
- Analysing a sample GL dataset to identify anomalies and reconciliation exceptions.
- Producing a controlled, AI-assisted review summary for finance management.
Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI for Finance
- Defining agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI supports finance operations and where human approval is 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.
- Linking agents to structured finance data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implementing workflows for automated variance investigation and management commentary.
- Supporting GL exception triage, reconciliation, and close-status monitoring.
- Enabling forecast refresh, scenario preparation, and 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.
- Establishing safe operating boundaries prior to production deployment.
Final Practical Capstone
- Integrating Python with AI, advanced analytics, and dashboard outputs into a single finance use case.
- Designing an agentic workflow that analyses results, flags exceptions, and prepares management insights.
- Presenting the workflow, controls, outputs, and recommended next steps.
Requirements
- A foundational grasp of finance, accounting, financial reporting, or FP&A principles.
- Experience with Excel and managing financial datasets.
- No prior Python programming background is necessary, though a basic familiarity with data analysis is advantageous.
- While not mandatory, a basic awareness of AI or generative AI tools such as ChatGPT, Microsoft Copilot, or Claude is beneficial.
- Comfort working with financial reports, KPIs, budgets, variances, and associated finance data.
- A laptop equipped with access to the necessary training tools, datasets, and approved AI platforms for practical sessions.
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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