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Duration 35 hours
Course Outline
AI Fundamentals and AI-Powered 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 specific applications in finance.
- Examining finance use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are best suited for AI assistance compared to controlled automation.
Python for Finance: Leveraging AI as a Coding Partner
- Core Python concepts for finance professionals, including variables, data types, conditional logic, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving away from isolated coding practices.
- Employing prompting techniques to ensure reliable generation of finance-focused code.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Performing filtering, grouping, aggregation, and calculation of financial metrics.
- Using AI to interpret errors, enhance logic, and document analysis steps.
Practical Applications of Finance Coding
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows supported by AI-driven code reviews.
- Validating outputs prior to their integration into financial reporting.
Practical Exercise
- Construct an AI-assisted Python workflow to analyze a sample financial dataset.
- Review the generated code, test assumptions, and refine outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality Assurance
- Cleaning, validating, and standardizing financial data.
- Addressing missing values, duplicates, inconsistent classifications, and date-related issues.
- Consolidating 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 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 financial data.
- Differentiating between useful signals and potentially misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Establishing historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analyses to support financial decision-making.
- Using AI to support scenario narratives while maintaining robust financial controls.
Practical Exercise
- Execute an end-to-end analysis of a financial dataset to identify key variances and anomalies.
- Prepare a concise, AI-assisted summary of financial insights backed by underlying data.
AI-Driven Financial Dashboards and Management Insights
Designing Finance Dashboards
- Selecting meaningful KPIs for financial, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than mere visual complexity.
- Structuring views for executive, management, and analyst audiences.
Developing 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 versus actual, profitability, cash flow, and performance.
AI-Enhanced Dashboarding
- Exploring financial data using natural language queries.
- Generating AI-assisted summaries and explanations for KPI movements.
- Utilizing AI to identify areas requiring deeper investigation.
Dashboard Controls and Reliability
- Considering data refresh rates, traceability, validation, and reconciliation.
- Managing access controls, sensitive financial information, and distribution protocols.
- Preventing misleading visuals or AI-generated conclusions.
Practical Exercise
- Develop an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial changes.
Advanced AI Tools for General Ledger and Finance Operations
AI Applications in General Ledger Management
- 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, and 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 processes before final reporting.
Practical Exercise
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Create a controlled, AI-assisted review summary for financial management.
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 remains critical.
- Comparing single-agent versus multi-step or multi-agent financial 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 financial 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 financial 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 foundational understanding of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency in Excel and experience handling financial datasets.
- While prior Python programming experience is not mandatory, basic familiarity with data analysis is advantageous.
- Basic knowledge of AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is recommended but not required.
- Comfort with navigating 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 sessions.
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