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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 the appropriate role for each in financial contexts.
- Exploring finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Identifying tasks suitable for AI assistance versus those requiring controlled automation.
Python for Finance - Leveraging AI as a Coding Partner
- Core Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving away from isolated coding practices.
- Developing prompting techniques for 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 financial metrics.
- Leveraging AI to explain errors, optimize logic, and document analysis steps.
Practical Applications of Finance Coding
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Creating reusable Python workflows with AI-supported code reviews.
- Validating outputs before integrating them into financial reporting.
Hands-on Application
- Construct an AI-assisted Python workflow to analyze a sample finance 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
- Cleaning, validating, and standardizing financial data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Consolidating data from multiple finance sources for comprehensive analysis.
Advanced Financial Analysis
- Examining 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 movements, patterns, and unusual transactions.
- Generating analytical questions and hypotheses based on financial data.
- Differentiating valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Utilizing historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to support scenario narratives while maintaining financial controls.
Hands-on Application
- Execute an end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted summary of financial insights 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 centered on decision-making questions rather than mere visual volume.
- Structuring views for executive, management, and analyst audiences.
Creating Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard utilization.
- Developing KPI cards, trends, variance visuals, drill-downs, and filters.
- Building views for budget versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Using natural language queries 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.
- Avoiding misleading visual or AI-generated conclusions.
Hands-on Application
- Build 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 General Ledger
- Analyzing GL accounts, transaction patterns, and posting behavior.
- 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.
- Prioritizing unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, and manual journal entries.
- Analyzing period-end journals and generating supporting explanations.
- Defining 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 before final reporting.
Hands-on 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 for Finance
- Defining the characteristics of 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.
- 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.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implementing automated variance investigation and management commentary workflows.
- Performing GL exception triage, reconciliation support, and close-status monitoring.
- Refreshing forecasts, preparing scenarios, and utilizing finance query assistants.
Governance, Risk, and Controls for Agentic AI
- Establishing human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Managing 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 analyzes results, flags exceptions, and prepares management insights.
- Present the workflow, controls, outputs, and recommended next steps
Requirements
- A foundational understanding of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency in Excel and experience working with financial datasets.
- No prior Python programming experience is necessary, though basic familiarity with data analysis is advantageous.
- Basic awareness of AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is recommended but not mandatory.
- Participants should feel at ease handling 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 hands-on sessions.
35 Hours
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