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Duration 35 hours
Course Outline
AI Fundamentals and Python with AI for Finance
AI, Analytics, and Agentic AI in Contemporary Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and understanding their respective roles in finance.
- Exploring financial use cases across accounting, FP&A, reporting, audit, treasury, and shared services.
- Identifying tasks suitable for AI assistance compared to those requiring controlled automation.
Python for Finance – AI as a Coding Partner
- Essential Python concepts for finance professionals, including variables, data types, conditions, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving away from isolated coding practices.
- Techniques for effective prompting to ensure reliable generation of finance-focused code.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Executing filtering, grouping, aggregation, and calculation of financial metrics.
- Using AI to interpret errors, enhance logic, and document analytical procedures.
Practical Python Applications in Finance
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows supported by AI-based code reviews.
- Verifying outputs before integrating them into financial reporting.
Practical Exercise
- Create an AI-assisted Python workflow to examine a sample finance dataset.
- Assess 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.
- 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 analyses.
- Performing drill-down analysis to pinpoint key financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to examine movements, patterns, and unusual transactions.
- Formulating analytical questions and hypotheses based on financial data.
- Differentiating significant signals from potentially misleading AI-generated interpretations.
Forecasting and Scenario Modeling
- Reviewing historical trends, drivers, and assumptions to inform forecasting.
- Conducting what-if and sensitivity analyses to support financial decisions.
- Using AI to assist with scenario narratives while maintaining financial controls.
Practical Exercise
- Conduct end-to-end analysis of a finance dataset to identify significant variances and anomalies.
- Compile a concise, AI-assisted summary of financial insights supported by underlying data.
AI-Based Financial Dashboards and Management Insights
Designing Finance Dashboards
- Selecting relevant KPIs for financial, managerial, and operational reporting.
- Designing dashboards centered on decision-making questions rather than visual clutter.
- Structuring views for executives, managers, and analysts.
Constructing Interactive Financial Dashboards
- Connecting and transforming financial data for dashboard integration.
- Creating KPI cards, trend lines, variance visuals, drill-down capabilities, and filters.
- Developing views for budget vs. actual, profitability, cash flow, and performance metrics.
Enhancing Dashboards with AI
- Using natural language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI fluctuations.
- Leveraging AI to highlight areas requiring deeper investigation.
Dashboard Controls and Reliability
- Considering data refresh rates, traceability, validation, and reconciliation.
- Managing access rights, sensitive financial information, and distribution controls.
- Preventing misleading visualizations 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.
Advanced AI Tools in General Ledger and Finance Operations
AI Applications in General Ledger
- Analyzing GL accounts, transaction patterns, and posting behaviors.
- Using AI to assist with transaction classification and account-level reviews.
- Detecting unusual, high-risk, or out-of-pattern entries.
AI for Reconciliation Processes
- Matching records and identifying exceptions across various 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 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 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 finance 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 financial 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 financial data and approved tools.
- Establishing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigations 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 needs, 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, controls, outputs, and recommended next steps
Requirements
- A foundational grasp of finance, accounting, financial reporting, or FP&A concepts.
- Proficiency in using Excel and handling financial datasets.
- No previous experience in Python programming is necessary, though a basic understanding of data analysis is advantageous.
- Basic familiarity with 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 associated financial data.
- A laptop with access to the required training tools, datasets, and authorized AI platforms should be available for practical sessions.
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