Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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 within finance.
- Exploring financial use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are best suited for AI assistance versus controlled automation.
Python for Finance – Leveraging AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Collaborating with AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Employing prompting techniques to ensure reliable, finance-focused code generation.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and computing financial metrics.
- Utilising AI to interpret errors, optimise logic, and document analytical steps.
Practical Applications of Finance Coding
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows supported by AI-driven code reviews.
- Validating outputs prior to their integration into financial reporting.
Practical Application
- Constructing an AI-assisted Python workflow to analyse a sample financial dataset.
- Reviewing generated code, testing assumptions, and refining outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardising financial data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Synthesising data from multiple financial sources for comprehensive analysis.
Advanced Financial Analysis
- Analysing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period analyses.
- Performing drill-down analyses to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate movements, patterns, and irregular transactions.
- Generating analytical questions and hypotheses from financial data.
- Differentiating meaningful signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Establishing historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to enhance scenario narratives while maintaining financial controls.
Practical Application
- Executing end-to-end analysis of a financial dataset to identify significant variances and anomalies.
- Producing a concise, AI-assisted summary of financial insights 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 sheer visual volume.
- Structuring views for executive, management, and analyst audiences.
Constructing Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard utilisation.
- Creating KPI cards, trend analyses, variance visuals, drill-downs, and filters.
- Building views for budget versus actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Utilising natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations of KPI fluctuations.
- Using AI to pinpoint areas requiring deeper investigative analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation requirements.
- Managing access, protecting sensitive financial information, and controlling distribution.
- Preventing misleading visualisations or AI-generated conclusions.
Practical Application
- Developing an interactive financial dashboard using a structured dataset.
- Incorporating AI-supported management commentary linked to quantifiable financial movements.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger Management
- Anlysing GL accounts, transaction patterns, and posting behaviours.
- Using AI to aid transaction classification and account-level reviews.
- Identifying unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Matching records and identifying exceptions across financial datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritising unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, or manual journal entries.
- 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.
- Generating AI-assisted variance explanations, commentary, and review notes.
- Implementing structured approval and validation before final reporting.
Practical Application
- Anlysing 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 the characteristics of agentic AI workflows: 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 financial workflows.
Designing Agentic Finance Workflows
- Creating agents for data collection, analysis, validation, and reporting tasks.
- Connecting agents to structured financial data and authorised tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implementing automated variance investigation and management commentary workflows.
- Facilitating GL exception triage, reconciliation support, and close-status monitoring.
- Managing forecast refreshes, 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.
- Defining safe operating boundaries prior to production deployment.
Final Practical Capstone
- Integrating Python with AI, advanced analytics, and dashboard outputs within a single financial use case.
- Designing an agentic workflow that analyses results, flags exceptions, and prepares management insights.
- Presenting the workflow, associated controls, outputs, and recommended next steps
Requirements
- A foundational understanding of finance, accounting, financial reporting, or FP&A principles.
- Proficiency with Excel and experience handling financial datasets.
- No previous experience in Python programming is necessary, though a basic familiarity with data analysis is advantageous.
- Familiarity with AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is beneficial but not mandatory.
- Participants should be at ease working with financial reports, KPIs, budgets, variances, and associated financial data.
- A laptop equipped with access to the necessary training tools, datasets, and authorised AI platforms is required for the 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 6500 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (1)
the tips and recommended prompts that we can take away from this training
