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Course Outline
Day 1: AI Foundations 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 financial applications 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 - Leveraging AI as a Coding Partner
- 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 away from isolated coding practices.
- Employing effective 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 computing financial metrics.
- Using AI to interpret errors, enhance logic, and document analytical procedures.
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 financial reporting.
Practical Application
- Construct an AI-assisted Python workflow to analyze a sample financial dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardizing financial data.
- Managing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Integrating data from multiple financial sources for comprehensive analysis.
Sophisticated 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 pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate fluctuations, patterns, and unusual transactions.
- Formulating analytical questions and hypotheses based on financial data.
- Distinguishing meaningful signals from misleading AI-generated interpretations.
Forecasting and Scenario Modeling
- Examining historical trends, drivers, and assumptions for forecasting purposes.
- Performing what-if and sensitivity analyses to support financial decision-making.
- Utilizing AI to enhance scenario narratives while maintaining financial controls.
Practical Application
- Execute an end-to-end analysis of a financial dataset to identify 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
Designing Financial Dashboards
- Selecting relevant KPIs for financial, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual complexity.
- Structuring views for executives, managers, and analysts.
Developing Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard integration.
- Creating KPI cards, trend lines, variance visuals, drill-downs, and filters.
- Building views for budget-versus-actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboard Features
- Using natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI movements.
- Employing AI to identify areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh rates, traceability, validation, and reconciliation.
- Managing access rights, sensitive financial information, and controlled distribution.
- Preventing 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 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.
- Utilizing AI to support transaction classification and account-level reviews.
- Identifying unusual, high-risk, or out-of-pattern entries.
AI in Reconciliation Processes
- Matching records and identifying exceptions across financial 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 Closing and Reporting
- Prioritizing closing 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 Application
- Analyze 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.
- Differentiating 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 financial data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigations 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.
- 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 analyzes results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps
Requirements
- Fundamental knowledge of finance, accounting principles, financial reporting, or FP&A concepts.
- Proficiency in Excel and experience handling financial datasets.
- No prior Python programming experience 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 recommended but not mandatory.
- Participants should feel confident navigating financial reports, KPIs, budgets, variances, and associated financial data.
- A laptop with access to the required training tools, datasets, and approved AI platforms must be available for practical sessions.
35 Hours
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