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Duration 35 hours
Course Outline
AI Fundamentals and Python with AI for Finance
AI, Analytics, and Agentic AI in Modern Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their specific roles in finance.
- Exploring financial use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Determining which tasks are suitable for AI assistance compared to controlled automation.
Python for Finance - Leveraging AI as a Coding Partner
- Essential Python concepts for finance professionals: variables, data types, conditional logic, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code instead of coding in isolation.
- Employing prompting techniques to ensure reliable generation of finance-focused code.
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 Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code reviews.
- Verifying outputs before integrating them into financial reporting.
Practical Application
- Constructing an AI-assisted Python workflow to analyze a sample finance dataset.
- Reviewing generated code, testing assumptions, and refining 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 discrepancies.
- Merging data from multiple financial sources for comprehensive analysis.
Sophisticated Financial Analysis
- Analysis of revenue, costs, margins, profitability, and working capital.
- Budget versus actual, variance, and period-over-period comparisons.
- Drill-down techniques to uncover key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Leveraging AI to investigate movements, patterns, and unusual transactions.
- Generating analytical questions and hypotheses from financial data.
- Differentiating between meaningful signals and misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Utilizing historical trends, drivers, and assumptions for forecasting.
- Conducting what-if and sensitivity analysis to support financial decisions.
- Employing AI to support scenario narratives while maintaining financial controls.
Practical Application
- Executing end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Creating a concise, AI-assisted financial insight summary backed by underlying data.
AI-Based Financial Dashboards and Management Insights
Designing Financial Dashboards
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards focused on decision-making questions rather than visual quantity.
- Structuring views for executives, management, and analysts.
Building Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard utilization.
- Creating KPI cards, trends, variance visuals, drill-downs, and filters.
- Developing views for budget versus 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.
- Utilizing AI to pinpoint areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation.
- Managing access, sensitive financial information, and controlled distribution.
- Avoiding misleading visual or AI-generated conclusions.
Practical Application
- Constructing an interactive financial dashboard using a structured dataset.
- Incorporating AI-supported management commentary linked to measurable financial movements.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analyzing GL accounts, transaction patterns, and posting behavior.
- 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 closing 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
- Analyzing a sample GL dataset to identify anomalies and reconciliation exceptions.
- Producing a controlled, AI-assisted review summary for finance management.
Agentic AI for Finance Operations and Decision Support
Understanding Agentic AI for Finance
- Defining agentic AI workflows: goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI supports finance operations and where human approval is 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
- Implementing automated variance investigation and management commentary workflows.
- Supporting GL exception triage, reconciliations, and close-status monitoring.
- Refreshing forecasts, preparing scenarios, and developing 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 risk, validation, and model limitations.
- Defining safe operating boundaries before production deployment.
Final Practical Capstone
- Integrating Python with AI, advanced analytics, and dashboard outputs into a single financial 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 foundational understanding of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency with Excel and experience managing financial datasets.
- No prior Python programming background 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 at ease working with financial reports, KPIs, budgets, variances, and related financial data.
- A laptop with access to the requisite training tools, datasets, and approved AI platforms must be available for practical sessions.
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