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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 understanding their respective roles in finance.
- Exploring finance use cases across accounting, FP&A, reporting, audit, treasury and shared services.
- Identifying tasks suitable for AI assistance versus those requiring controlled automation.
Python for Finance - Using AI as a Coding Partner
- Python fundamentals for finance professionals: variables, data types, conditions, functions and notebooks.
- Employing AI assistants to generate, explain, debug and refine Python code, moving away from isolated coding practices.
- Developing 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 computing finance metrics.
- Utilising AI to explain errors, refine logic and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis and ratio analysis.
- Creating reusable Python workflows supported by AI-assisted code review.
- Validating outputs prior to their use in finance reporting.
Hands-on Application
- Developing an AI-assisted Python workflow to analyse a sample finance dataset.
- Reviewing generated code, testing assumptions and enhancing 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
- Analyzing revenue, cost, margin, profitability and working capital.
- Conducting budget versus actual, variance and period-over-period analysis.
- Performing drill-down analysis to pinpoint 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.
- Differentiating useful signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examining historical trends, drivers and assumptions for forecasting.
- Conducting what-if and sensitivity analysis to support financial decisions.
- Leveraging AI to support scenario narratives while maintaining financial controls.
Hands-on Application
- Carrying out end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Compiling a concise AI-assisted finance insight summary supported by underlying data.
AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting meaningful KPIs for finance, management and operational reporting.
- Designing dashboards centred on decision questions rather than visual volume.
- Structuring views for executive, management and analyst audiences.
Building Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard utilisation.
- 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.
- Employing AI to identify areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation and reconciliation.
- Managing access, sensitive financial information and controlled distribution.
- Avoiding misleading visual or AI-generated conclusions.
Hands-on Application
- Creating an interactive financial dashboard using a structured dataset.
- Incorporating AI-supported management commentary linked to measurable financial movements.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analyzing 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
- Detecting duplicate, unusual and manual journals.
- Analyzing 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.
- Applying structured approval and validation before final reporting.
Hands-on Application
- Analyzing a sample GL dataset to identify anomalies and reconciliation exceptions.
- Generating 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 can support finance operations and where human approval remains critical.
- Distinguishing between single-agent and 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.
- Handling 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.
- Managing data confidentiality, hallucination risk, validation and model limitations.
- Defining 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 understanding of finance, accounting, financial reporting or FP&A concepts.
- Experience with Excel and handling financial datasets.
- No prior Python programming experience is necessary, though basic exposure to data analysis is advantageous.
- Basic familiarity with AI or generative AI tools such as ChatGPT, Microsoft Copilot or Claude is helpful but not mandatory.
- Participants should feel at ease working with financial reports, KPIs, budgets, variances and related finance data.
- Access to a laptop equipped with the necessary training tools, datasets and approved AI platforms is required for practical sessions.
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