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.
Course Outline
Day 1: AI Fundamentals and Python with AI for Finance
AI, Analytics and Agentic AI in Modern Finance
- How generative AI, machine learning, automation and agentic AI differ and where each fits in finance.
- Finance use cases across accounting, FP&A, reporting, audit, treasury and shared services.
- Identifying suitable tasks for AI assistance versus controlled automation.
Python for Finance - Using AI as a Coding Partner
- Python basics for finance professionals: variables, data types, conditions, functions and notebooks.
- Using AI assistants to generate, explain, debug and refine Python code rather than coding in isolation.
- Prompting techniques for reliable finance-focused code generation.
Working with Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating and calculating finance metrics.
- Using AI to explain errors, improve 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 review.
- Validating outputs before using them in finance reporting.
Hands-on Application
- Build an AI-assisted Python workflow to analyse a sample finance dataset.
- Review generated code, test assumptions and improve the output with human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality
- Cleaning, validating and standardising finance data.
- Handling missing values, duplicates, inconsistent classifications and date issues.
- Combining data from multiple finance sources for analysis.
Advanced Financial Analysis
- Revenue, cost, margin, profitability and working-capital analysis.
- Budget versus actual, variance and period-over-period analysis.
- Drill-down analysis to identify key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Using AI to investigate movements, patterns and unusual transactions.
- Generating analytical questions and hypotheses from finance data.
- Distinguishing useful signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Historical trends, drivers and assumptions for forecasting.
- What-if and sensitivity analysis for finance decision support.
- Using AI to support scenario narratives while preserving financial controls.
Hands-on Application
- Perform end-to-end analysis of a finance dataset and identify key variances and anomalies.
- Prepare a concise AI-assisted finance insight summary supported by the underlying data.
Day 3: AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting meaningful KPIs for finance, management and operational reporting.
- Designing dashboards around decision questions rather than visual volume.
- Structuring executive, management and analyst views.
Building Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard use.
- Creating KPI cards, trends, variance visuals, drill-downs and filters.
- Building views for budget versus actual, profitability, cash flow and performance.
AI-Enhanced Dashboarding
- Using natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations of KPI movements.
- Using AI to identify areas that require deeper analysis.
Dashboard Controls and Reliability
- Data refresh, traceability, validation and reconciliation considerations.
- Managing access, sensitive financial information and controlled distribution.
- Avoiding misleading visual or AI-generated conclusions.
Hands-on Application
- Build an interactive financial dashboard using a structured dataset.
- Add AI-supported management commentary linked to measurable financial movements.
Day 4: Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analysing GL accounts, transaction patterns and posting behaviour.
- Using AI to support transaction classification and account-level review.
- 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
- Duplicate, unusual and manual journal detection.
- Period-end journal analysis and supporting explanation generation.
- Risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Close task prioritisation and exception-based review.
- AI-assisted variance explanations, commentary and review notes.
- Using structured approval and validation before final reporting.
Hands-on Application
- Analyse a sample GL dataset and 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 for Finance
- What makes an AI workflow agentic: goals, planning, tools, memory, actions and feedback loops.
- Where agentic AI can support finance operations and where human approval remains essential.
- 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
- Automated variance investigation and management commentary workflows.
- GL exception triage, reconciliation support and close-status monitoring.
- Forecast refresh, scenario preparation and finance query assistants.
Governance, Risk and Controls for Agentic AI
- Human-in-the-loop controls, audit trails, permissions and segregation of duties.
- Data confidentiality, hallucination risk, validation and model limitations.
- Defining safe operating boundaries before production deployment.
Final Practical Capstone
- Combine Python with AI, advanced analytics and dashboard outputs in one 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
- Basic understanding of finance, accounting, financial reporting or FP&A concepts.
- Familiarity with Excel and working with financial datasets.
- No prior Python programming experience is required, although basic exposure to data analysis is helpful.
- Basic awareness of AI or generative AI tools such as ChatGPT, Microsoft Copilot or Claude is beneficial but not essential.
- Participants should be comfortable working with financial reports, KPIs, budgets, variances and related finance data.
- A laptop with access to the required training tools, datasets and approved AI platforms should be available for the hands-on sessions.
35 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
