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Course Outline
Day 1: AI Fundamentals and AI-Powered Python for Finance
AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their specific roles within finance.
- Exploring finance 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 – Utilising AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Using AI assistants to generate, explain, debug, and refine Python code, fostering a collaborative approach rather than coding in isolation.
- Applying prompting techniques to ensure reliable and finance-focused code generation.
Managing Financial Data with Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and computing financial metrics.
- Leveraging AI to explain errors, optimise logic, and document analytical steps.
Practical Applications of Finance Coding
- Automating routine calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows supported by AI-driven code reviews.
- Validating outputs to ensure accuracy before integration into financial reporting.
Practical Application
- Construct an AI-assisted Python workflow to analyse a sample finance dataset.
- Review the generated code, test underlying assumptions, and refine outputs through human validation.
Day 2: 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.
- Integrating data from multiple financial sources for comprehensive analysis.
Advanced Financial Analysis
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period analyses.
- Performing drill-down analyses to identify key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate movements, patterns, and irregular transactions.
- Generating analytical questions and hypotheses based on financial data.
- Differentiating between useful signals and potentially misleading AI interpretations.
Forecasting and Scenario Analysis
- Reviewing historical trends, drivers, and assumptions for forecasting purposes.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to support scenario narratives while maintaining strict financial controls.
Practical Application
- Execute an end-to-end analysis of a finance dataset to pinpoint key variances and anomalies.
- Prepare a concise AI-assisted summary of financial insights, supported by underlying data.
Day 3: AI-Driven 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 mere visual density.
- Structuring views for executives, management, and analysts.
Constructing Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard implementation.
- Creating KPI cards, trend analyses, variance visualizations, drill-down features, and filters.
- Building views for budget versus actual, profitability, cash flow, and overall performance.
AI-Enhanced Dashboarding
- Utilising natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations for KPI fluctuations.
- Using AI to identify areas requiring deeper analytical scrutiny.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation processes.
- Managing access, sensitive financial information, and controlled distribution.
- Avoiding misleading visual interpretations or AI-generated conclusions.
Practical Application
- Build an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial changes.
Day 4: Advanced AI Tools for General Ledger and Finance Operations
AI Applications in General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviours.
- 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 financial datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritising unreconciled items for manual investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, or manual journal entries.
- Analyzing period-end journal entries 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
- Analyse a sample GL dataset to identify anomalies and reconciliation exceptions.
- Create 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 is 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
- Automating variance investigations and generating management commentary.
- Triaging GL exceptions, supporting reconciliations, and monitoring close status.
- Refreshing forecasts, preparing scenarios, and assisting with finance queries.
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
- Integrate Python, AI, advanced analytics, and dashboard outputs into 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
- Fundamental knowledge of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency in using Excel and managing financial datasets.
- No prior experience in Python programming is necessary, though basic familiarity with data analysis is advantageous.
- Basic familiarity with AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is beneficial but not mandatory.
- Participants should be comfortable navigating financial reports, KPIs, budgets, variances, and related financial data.
- A laptop equipped with access to the necessary training tools, datasets, and approved AI platforms is required for practical sessions.
35 Hours
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