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Course Outline
Day 1: AI Fundamentals and Python with AI for Finance
AI, Analytics, and Agentic AI in Modern Finance
- Understand the distinctions between generative AI, machine learning, automation, and agentic AI, and identify their respective roles in finance.
- Explore finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Distinguish tasks suitable for AI assistance from those requiring controlled automation.
Python for Finance - Using AI as a Coding Partner
- Master Python basics for finance professionals: variables, data types, conditions, functions, and notebooks.
- Utilize AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding.
- Employ prompting techniques for reliable, finance-focused code generation.
Working with Financial Data in Python
- Import Excel and CSV data using Pandas and DataFrames.
- Filter, group, aggregate, and calculate finance metrics.
- Leverage AI to explain errors, improve logic, and document analysis steps.
Practical Finance Coding Applications
- Automate repetitive calculations, variance analysis, and ratio analysis.
- Create reusable Python workflows with AI-supported code review.
- Validate outputs before integrating them into finance 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
- Clean, validate, and standardize finance data.
- Address missing values, duplicates, inconsistent classifications, and date issues.
- Integrate data from multiple finance sources for comprehensive analysis.
Advanced Financial Analysis
- Perform revenue, cost, margin, profitability, and working-capital analysis.
- Conduct budget versus actual, variance, and period-over-period analysis.
- Execute drill-down analysis to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Utilize AI to investigate movements, patterns, and unusual transactions.
- Generate analytical questions and hypotheses from finance data.
- Differentiate useful signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examine historical trends, drivers, and assumptions for forecasting.
- Apply what-if and sensitivity analysis for finance decision support.
- Use AI to support scenario narratives while maintaining financial controls.
Hands-on Application
- Conduct 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.
Day 3: AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Select meaningful KPIs for finance, management, and operational reporting.
- Design dashboards centered on decision questions rather than visual volume.
- Structure views for executive, management, and analyst audiences.
Building Interactive Financial Dashboards
- Connect and transform finance data for dashboard implementation.
- Create KPI cards, trends, variance visuals, drill-downs, and filters.
- Develop views for budget versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Explore financial data using natural-language querying.
- Generate AI-assisted summaries and explanations of KPI movements.
- Use AI to identify areas requiring deeper analysis.
Dashboard Controls and Reliability
- Consider data refresh, traceability, validation, and reconciliation.
- Manage access, sensitive financial information, and controlled distribution.
- Avoid misleading visual or AI-generated conclusions.
Hands-on Application
- Build an interactive financial dashboard using a structured dataset.
- Add 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
- Analyze GL accounts, transaction patterns, and posting behavior.
- Use AI to support transaction classification and account-level review.
- Identify unusual, high-risk, or out-of-pattern entries.
AI for Reconciliations
- Match records and identify exceptions across finance datasets.
- Support bank, intercompany, and balance-sheet reconciliations.
- Prioritize unreconciled items for human investigation.
Journal Entry Analytics
- Detect duplicate, unusual, and manual journals.
- Analyze period-end journals and generate supporting explanations.
- Establish risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Prioritize close tasks and conduct exception-based review.
- Use AI-assisted variance explanations, commentary, and review notes.
- Implement 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
- Define the characteristics of agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Determine where agentic AI supports finance operations and where human approval is essential.
- Differentiate between single-agent and multi-step or multi-agent finance workflows.
Designing Agentic Finance Workflows
- Create agents for data collection, analysis, validation, and reporting tasks.
- Connect agents to structured finance data and approved tools.
- Design escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implement automated variance investigation and management commentary workflows.
- Utilize GL exception triage, reconciliation support, and close-status monitoring.
- Leverage forecast refresh, scenario preparation, and finance query assistants.
Governance, Risk, and Controls for Agentic AI
- Implement human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Address data confidentiality, hallucination risk, validation, and model limitations.
- Define safe operating boundaries before 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.
- Familiarity with Excel and handling financial datasets.
- No prior Python programming experience is required, though basic exposure to 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 proficient in working with financial reports, KPIs, budgets, variances, and related finance data.
- A laptop with access to the required training tools, datasets, and approved AI platforms should be available for hands-on sessions.
35 Hours
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