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Course Outline
Day 1: AI Fundamentals and AI-Enhanced Python for Finance
The Role of AI, Analytics, and Agentic AI in Modern Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in finance.
- Exploring finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are best suited for AI assistance versus controlled automation.
Python for Finance: Collaborating with AI
- Essential Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
- Using AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Employing prompting techniques to achieve reliable, finance-focused code generation.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating finance metrics.
- Utilizing AI to explain errors, enhance logic, and document analysis steps.
Practical Applications of Finance Coding
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code reviews.
- Validating outputs before integrating them into finance reporting.
Practical Exercise
- Construct an AI-assisted Python workflow to analyze a sample finance dataset.
- Review generated code, test assumptions, and refine output through human validation.
Day 2: Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardizing finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date-related issues.
- Consolidating data from multiple finance sources for comprehensive analysis.
Sophisticated Financial Analysis
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget-vs-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 from finance data.
- Distinguishing valuable insights from misleading AI-generated interpretations.
Forecasting and Scenario Modeling
- Examining historical trends, drivers, and assumptions for forecasting.
- Performing what-if and sensitivity analyses to support finance decisions.
- Using AI to craft scenario narratives while maintaining financial controls.
Practical Exercise
- Conduct an end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Create a concise, AI-assisted summary of finance insights backed by underlying data.
Day 3: AI-Driven Financial Dashboards and Management Insights
Finance Dashboard Design Principles
- Selecting relevant KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual complexity.
- Structuring views for executives, managers, and analysts.
Creating Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard implementation.
- Developing KPI cards, trends, variance visuals, drill-downs, and filters.
- Building views for budget vs. actual, profitability, cash flow, and performance metrics.
Enhancing Dashboards with AI
- Utilizing natural-language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI fluctuations.
- Leveraging AI to identify areas requiring deeper investigation.
Dashboard Governance and Reliability
- Considering data refresh, traceability, validation, and reconciliation.
- Managing access, sensitive financial information, and controlled distribution.
- Avoiding misleading visuals 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 Applications in General Ledger and Finance Operations
AI in General Ledger Management
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Using AI to aid transaction classification and account-level reviews.
- Identifying unusual, high-risk, or out-of-pattern entries.
AI for Reconciliation Processes
- 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, or manual journals.
- Analyzing period-end journals and generating supporting explanations.
- Identifying risk indicators and review checkpoints for finance teams.
AI in Financial Closing 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 Exercise
- 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
Comprehending Agentic AI in Finance
- Understanding the components of agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI can support finance operations and where human approval is 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.
- Integrating agents with structured finance data and approved tools.
- Defining escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigation and management commentary workflows.
- Triaging GL exceptions, supporting reconciliations, and monitoring close status.
- 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 Capstone Project
- Synthesizing Python with AI, advanced analytics, and dashboard outputs into a single finance use case.
- Designing an agentic workflow that analyzes results, flags exceptions, and prepares management insights.
- Presenting the workflow, controls, outputs, and recommended next steps.
Requirements
- A fundamental grasp of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency with Excel and experience working with financial datasets.
- No prior Python programming experience is necessary, though basic familiarity with data analysis is advantageous.
- Basic knowledge of AI or generative AI tools such as ChatGPT, Microsoft Copilot, or Claude is beneficial but not mandatory.
- Participants should be confident in handling financial reports, KPIs, budgets, variances, and associated finance 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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