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Course Outline
Day 1: AI Fundamentals and AI-Powered Python for Finance
The Role of AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying where each fits within finance.
- Exploring finance use cases spanning 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 as a Coding Partner
- Core Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, fostering collaboration rather than isolated coding.
- Employing effective prompting techniques to ensure reliable, finance-focused code generation.
Managing Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and computing key finance metrics.
- Leveraging AI to explain errors, enhance logic, and document analysis procedures.
Practical Applications in Finance Coding
- Automating routine calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code reviews.
- Validating outputs to ensure accuracy before integration into finance reporting.
Hands-on Practice
- Construct an AI-assisted Python workflow to analyze a sample finance dataset.
- Review the generated code, test assumptions, and refine outputs through human validation.
Day 2: AI-Driven Advanced Financial Data Analysis
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardizing finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Merging data from various finance sources for comprehensive analysis.
Advanced Financial Analysis Techniques
- Analyzing revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period analyses.
- Performing drill-down analyses to pinpoint critical financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Using AI to investigate fluctuations, patterns, and irregular transactions.
- Formulating analytical questions and hypotheses based on finance data.
- Differentiating valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Modeling
- Examining historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decisions.
- Utilizing AI to support scenario narratives while maintaining strict financial controls.
Hands-on Practice
- Execute an end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted summary of finance insights backed by underlying data.
Day 3: AI-Enhanced Financial Dashboards and Management Insights
Strategic Finance Dashboard Design
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than just visual complexity.
- Structuring views for executives, management, and analysts.
Developing Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Creating KPI cards, trend lines, variance visuals, drill-downs, and filters.
- Building views for budget versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding Capabilities
- Utilizing natural-language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI movements.
- Leveraging AI to highlight areas requiring deeper analysis.
Dashboard Governance and Reliability
- Considering data refresh cycles, traceability, validation, and reconciliation.
- Managing access rights, sensitive financial information, and controlled distribution.
- Preventing misleading visual or AI-generated conclusions.
Hands-on Practice
- Construct 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 General Ledger Management
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Supporting transaction classification and account-level reviews with AI.
- 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.
- 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 approvals and validations before final reporting.
Hands-on Practice
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Create 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 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 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 finance data and approved tools.
- Establishing 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 refreshes, 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 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 foundational grasp of finance, accounting, financial reporting, or FP&A principles.
- Proficiency in Excel and experience handling financial datasets.
- No prior Python programming experience is necessary, though basic familiarity with data analysis is advantageous.
- General awareness of AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is helpful but not mandatory.
- Comfortable working with financial reports, KPIs, budgets, variances, and related finance data.
- Access to a laptop equipped with the necessary training tools, datasets, and approved AI platforms for hands-on sessions.
35 Hours
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