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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
- 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.
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.
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.
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.
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.
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