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Course Outline
Day 1: AI Fundamentals and AI-Assisted Python for Finance
AI, Analytics, and Agentic AI in Contemporary Finance
- Differentiating generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in finance.
- Exploring finance applications across accounting, FP&A, reporting, audit, treasury, and shared services.
- Distinguishing tasks suitable for AI assistance versus those requiring controlled automation.
Python for Finance - Leveraging AI as a Coding Companion
- Essential Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Employing effective prompting techniques for reliable, finance-oriented code generation.
Handling Financial Data with Python
- Importing Excel and CSV files using Pandas and DataFrames.
- Filtering, grouping, aggregating, and computing finance metrics.
- Applying AI to clarify errors, enhance logic, and document analytical steps.
Practical Applications of Finance Coding
- Automating routine calculations, variance analysis, and ratio computations.
- Developing reusable Python workflows with AI-supported code review.
- Verifying outputs prior to their use in finance reporting.
Practical Exercise
- Develop an AI-assisted Python workflow to analyze a sample finance dataset.
- Review the generated code, test assumptions, and refine outputs through human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Integrity
- Cleaning, validating, and standardizing 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
- Leveraging AI to investigate fluctuations, patterns, and irregular transactions.
- Formulating analytical questions and hypotheses based on finance data.
- Differentiating valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Modelling
- Identifying historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to enhance scenario narratives while maintaining financial controls.
Practical Exercise
- Execute an end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Prepare a concise, AI-assisted summary of finance insights backed by underlying data.
Day 3: AI-Driven Financial Dashboards and Management Insights
Designing Finance Dashboards
- Selecting relevant KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual complexity.
- Structuring views for executive, management, and analyst audiences.
Creating Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Creating KPI cards, trend lines, variance visuals, drill-downs, and filters.
- Developing views for budget-vs-actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Utilizing natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI movements.
- Identifying areas requiring deeper analysis through AI insights.
Dashboard Governance and Reliability
- Considering data refresh, traceability, validation, and reconciliation requirements.
- Managing access rights, sensitive financial information, and controlled distribution.
- Preventing misleading visual or AI-generated conclusions.
Practical Exercise
- Build an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial changes.
Day 4: Advanced AI Tools in General Ledger and Finance Operations
AI Applications in the General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Using AI to support 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.
- Supporting bank, intercompany, and balance sheet reconciliations.
- Prioritizing unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, and manual journal entries.
- Analyzing period-end journals and generating supporting explanations.
- Establishing risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Prioritizing close tasks and conducting exception-based reviews.
- Generating AI-assisted variance explanations, commentary, and review notes.
- Implementing structured approval and validation processes before final reporting.
Practical Exercise
- Analyze 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 supports finance operations and where human approval is critical.
- Distinguishing between single-agent and multi-step or multi-agent finance workflows.
Architecting Agentic Finance Workflows
- Creating agents for data collection, analysis, validation, and reporting tasks.
- Integrating agents with structured finance data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigation and management commentary workflows.
- Triage of GL exceptions, 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.
- Addressing data confidentiality, hallucination risks, validation, and model limitations.
- Establishing safe operating boundaries prior to production deployment.
Final Practical Capstone
- Synthesize Python, AI, advanced analytics, and dashboard outputs into a single finance use case.
- Design an agentic workflow that analyzes results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps
Requirements
- A foundational grasp of finance, accounting, financial reporting, or FP&A principles.
- Proficiency in Excel and experience handling financial datasets.
- No prior experience in Python programming is necessary, though basic familiarity with data analysis is advantageous.
- General knowledge of AI or generative AI platforms such as ChatGPT, Microsoft Copilot, or Claude is recommended but not mandatory.
- Confidence in working with financial statements, KPIs, budgets, variances, and associated finance data.
- Availability of a laptop equipped with access to necessary training tools, datasets, and approved AI platforms for practical sessions.
35 Hours
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