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Duration 35 hours
Course Outline
AI Fundamentals and AI-Assisted Python for Finance
AI, Analytics, and Agentic AI in Modern Finance
- Exploring the distinctions between generative AI, machine learning, automation, and agentic AI, and their respective roles in finance.
- Finance use cases spanning 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, conditions, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Prompting strategies for reliable generation of finance-focused code.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating financial metrics.
- Employing AI to explain errors, enhance logic, and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code reviews.
- Validating outputs before integration into finance reporting.
Practical Application
- Develop an AI-assisted Python workflow to analyze a sample financial dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality
- Cleaning, validating, and standardizing financial data.
- Managing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Consolidating data from multiple financial sources for comprehensive analysis.
Advanced Financial Analysis
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget vs. actual, variance, and period-over-period analyses.
- Performing drill-down analysis to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Using AI to investigate movements, patterns, and unusual transactions.
- Formulating analytical questions and hypotheses from financial data.
- Distinguishing meaningful signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examining historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decisions.
- Utilizing AI to enhance scenario narratives while maintaining financial controls.
Practical Application
- Execute end-to-end analysis of a financial dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted financial insight summary backed by underlying data.
AI-Driven Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting meaningful KPIs for financial, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual clutter.
- Structuring views for executives, management, and analysts.
Constructing Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard utilization.
- Creating KPI cards, trends, variance visuals, drill-downs, and filters.
- Building views for budget vs. actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Using natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations of KPI fluctuations.
- 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 Application
- Build an interactive financial dashboard using a structured dataset.
- Integrate AI-supported management commentary linked to measurable financial movements.
Advanced AI Tools in General Ledger and Financial Operations
AI Applications in General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- 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, or 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 before final reporting.
Practical Application
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Create a controlled, AI-assisted review summary for finance management.
Agentic AI for Financial 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.
- Triaging GL exceptions, supporting reconciliations, and monitoring close status.
- Refreshing forecasts, preparing scenarios, and providing finance query assistance.
Governance, Risk, and Controls for Agentic AI
- Implementing human-in-the-loop controls, audit trails, permissions, and segregation of duties.
- Managing data confidentiality, hallucination risks, validation, and model limitations.
- Defining safe operating boundaries prior to production deployment.
Final Practical Capstone
- Integrate Python with AI, advanced analytics, and dashboard outputs in 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 fundamental understanding of finance, accounting, financial reporting, or FP&A concepts.
- Familiarity with Excel and experience working with financial datasets.
- No prior Python programming experience is necessary, 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 at ease with financial reports, KPIs, budgets, variances, and related financial data.
- A laptop with access to required training tools, datasets, and approved AI platforms must be available for practical sessions.
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