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Duration 35 hours
Course Outline
AI Fundamentals and AI-Powered Python for Finance
AI, Analytics, and Agentic AI in Modern Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their specific 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: AI as a Coding Partner
- Essential Python concepts for finance professionals, including variables, data types, conditions, functions, and notebooks.
- Leveraging AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Applying prompting techniques to ensure reliable, finance-focused code generation.
Managing Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Executing filtering, grouping, aggregation, and finance metric calculations.
- Utilising AI to troubleshoot errors, optimise logic, and document analysis procedures.
Practical Applications in Finance Coding
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code review.
- Validating outputs prior to their use in financial reporting.
Practical Exercise
- Construct an AI-assisted Python workflow to analyse a sample finance dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
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
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period comparisons.
- Performing drill-down analysis to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate fluctuations, patterns, and irregular transactions.
- Generating analytical questions and hypotheses directly from finance data.
- Differentiating valuable signals from potentially misleading AI-generated interpretations.
Forecasting and Scenario Modelling
- Examining historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support finance decisions.
- Using AI to enhance scenario narratives while maintaining financial controls.
Practical Exercise
- Execute end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted finance insight summary backed by underlying data.
AI-Driven Financial Dashboards and Management Insights
Finance Dashboard Design Principles
- Selecting relevant KPIs for finance, management, and operational reporting.
- Designing dashboards centred on decision-making questions rather than mere visual complexity.
- Structuring views for executives, management, and analysts.
Creating Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard consumption.
- Developing KPI cards, trends, variance visuals, drill-downs, and filters.
- Building views for budget vs. actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Utilising natural-language querying to explore financial data.
- Generating AI-assisted summaries and explanations for 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.
- Preventing misleading visual or AI-generated conclusions.
Practical Exercise
- Construct an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial changes.
Advanced AI Tools for General Ledger and Finance Operations
AI Applications in General Ledger
- Analysing GL accounts, transaction patterns, and posting behaviour.
- Using AI to assist in transaction classification and account-level reviews.
- Detecting unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Matching records and identifying exceptions across finance datasets.
- Assisting with bank, intercompany, and balance-sheet reconciliations.
- Prioritising unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, and manual journal entries.
- Analysing 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.
- Generating AI-assisted variance explanations, commentary, and review notes.
- Implementing structured approval and validation before final reporting.
Practical Exercise
- Analyse a sample GL dataset to identify anomalies and reconciliation exceptions.
- Prepare a controlled, AI-assisted review summary for finance management.
Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI in Finance
- Defining the agentic nature of AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI supports finance operations and where human approval remains critical.
- Comparing 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.
- Establishing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigation and management commentary workflows.
- Managing GL exception triage, reconciliation support, and close-status monitoring.
- Refreshing forecasts, preparing scenarios, and developing 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 prior to production deployment.
Final Practical Capstone
- Integrate Python with 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.
- Experience with Excel and handling financial datasets.
- No prior Python programming background is required, though basic familiarity with data analysis is advantageous.
- Basic awareness of AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is beneficial but not mandatory.
- Participants should be at ease working with financial reports, KPIs, budgets, variances, and related finance data.
- A laptop with access to necessary training tools, datasets, and approved AI platforms should be available for practical sessions.
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