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Course Outline
Day 1: AI Fundamentals and Python with AI for Finance
AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in finance.
- Exploring finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are suitable for AI assistance versus those requiring controlled automation.
Python for Finance – Utilising AI as a Coding Partner
- Foundational Python skills 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.
- Mastering 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 calculating key finance metrics.
- Using AI to elucidate errors, optimise logic, and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code review.
- Validating outputs prior to their use in finance reporting.
Practical Application
- Construct an AI-assisted Python workflow to analyse a sample finance dataset.
- Review generated code, test assumptions, and refine the output through human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardising finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Integrating data from multiple finance sources for comprehensive analysis.
Sophisticated Financial Analysis
- Analsing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period analyses.
- 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 derived from finance data.
- Differentiating valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examining historical trends, drivers, and assumptions for forecasting.
- Applying what-if and sensitivity analysis to support financial decisions.
- Utilising AI to support scenario narratives while maintaining financial controls.
Practical Application
- Execute end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Prepare a concise, AI-assisted finance insight summary backed by 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 focused on decision-making questions rather than visual volume.
- Structuring views for executives, management, and analysts.
Creating Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Building KPI cards, trends, variance visuals, drill-downs, and filters.
- Developing views for budget versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Utilising natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations of KPI movements.
- Leveraging 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.
Practical Application
- Construct an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial movements.
Day 4: Advanced AI Tools in General Ledger and Finance Operations
AI Applications in the General Ledger
- Analysing GL accounts, transaction patterns, and posting behaviour.
- Using AI to support transaction classification and account-level reviews.
- 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 journal entries.
- Conducting period-end journal analysis 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.
Practical Application
- Analyse a sample GL dataset to 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 in 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.
- Managing GL exception triage, reconciliation support, and close-status monitoring.
- Refreshing forecasts, preparing scenarios, and using 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 risk, validation, and model limitations.
- Defining safe operating boundaries before production deployment.
Final Practical Capstone
- Integrate Python with AI, advanced analytics, and dashboard outputs within a single 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
- A fundamental grasp of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency with Excel and the ability to work with financial datasets.
- No prior Python programming experience is mandatory, though 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 a prerequisite.
- Participants should be at ease handling financial reports, KPIs, budgets, variances, and related finance data.
- A laptop with access to necessary training tools, datasets, and approved AI platforms is required for practical sessions.
35 Hours
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