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Course Outline
Day 1: AI Fundamentals and Python for Finance with AI Support
AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their specific roles within 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 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.
- Collaborating with AI assistants to generate, explain, debug, and refine Python code, rather than coding in isolation.
- Employing effective prompting techniques to ensure reliable, finance-specific code generation.
Managing Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating key financial metrics.
- Using AI to clarify errors, enhance logic, and document analytical steps.
Practical Python Applications in Finance
- Automating repetitive calculations, variance analysis, and ratio computations.
- Developing reusable Python workflows with AI-supported code reviews.
- Verifying outputs to ensure accuracy before integrating them into financial reporting.
Practical Exercise
- Develop an AI-assisted Python workflow to process a sample finance dataset.
- Examine the generated code, test assumptions, and refine outputs through human validation.
Day 2: Advanced Financial Data Analysis with AI
Preparing and Ensuring Financial Data Quality
- Cleaning, validating, and standardizing finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Merging data from multiple financial sources for comprehensive analysis.
In-Depth Financial Analysis
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget-versus-actual, variance, and period-over-period comparisons.
- Performing drill-down analyses to pinpoint critical financial drivers.
AI-Enhanced Analysis and Anomaly Detection
- Leveraging AI to investigate fluctuations, patterns, and irregular transactions.
- Formulating analytical questions and hypotheses based on financial data.
- Differentiating between valuable signals and misleading AI-generated interpretations.
Forecasting and Scenario Modeling
- Evaluating historical trends, drivers, and assumptions for forecasting purposes.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to enhance scenario narratives while maintaining strict financial controls.
Practical Exercise
- Execute an end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Compile a concise, AI-assisted summary of financial insights backed by the underlying data.
Day 3: AI-Powered Financial Dashboards and Management Insights
Designing Finance Dashboards
- Selecting relevant KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than mere visual complexity.
- Structuring views for executives, managers, and analysts.
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 monitoring.
Enhancing Dashboards with AI
- Using natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations of KPI movements.
- Utilizing AI to identify areas requiring deeper investigation.
Ensuring Dashboard Reliability and Control
- Considering data refresh rates, traceability, validation, and reconciliation.
- Managing access, handling sensitive financial information, and controlling distribution.
- Preventing 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 Tools for General Ledger and Finance Operations
AI Applications in the General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Using AI to aid in transaction classification and account-level reviews.
- Detecting 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 review.
Journal Entry Analytics
- Detecting duplicate, unusual, or manual journals.
- Performing period-end journal analysis and generating supporting explanations.
- Identifying 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 steps 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
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 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.
- Connecting agents to structured financial data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Workflows for automated variance investigation and management commentary.
- GL exception triage, reconciliation support, and close-status monitoring.
- Forecast updates, scenario preparation, and 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, associated controls, outputs, and recommended next steps.
Requirements
- A fundamental grasp of finance, accounting, financial reporting, or FP&A principles.
- Proficiency with Excel and experience handling financial datasets.
- While prior Python programming experience is not mandatory, basic familiarity with data analysis is advantageous.
- General knowledge of AI or generative AI platforms such as ChatGPT, Microsoft Copilot, or Claude is beneficial but not a strict requirement.
- Confidence in working with financial reports, KPIs, budgets, variances, and related financial data.
- Access to a laptop equipped with the necessary training tools, datasets, and approved AI platforms for practical exercises.
35 Hours
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