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Duration 35 hours
Course Outline
AI Fundamentals and AI-Powered Python for Finance
AI, Analytics, and Agentic AI in Modern Finance
- Distinguishing between generative AI, machine learning, automation, and agentic AI, and identifying their respective roles in 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 controlled automation.
Python for Finance - Leveraging AI as a Coding Partner
- Foundational Python concepts for finance professionals: variables, data types, conditions, functions, and notebooks.
- Utilizing AI assistants to generate, explain, debug, and refine Python code, moving beyond isolated coding practices.
- Applying prompting techniques to ensure reliable code generation focused on finance tasks.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Filtering, grouping, aggregating, and calculating key finance metrics.
- Leveraging AI to explain errors, enhance logic, and document analysis steps.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Creating reusable Python workflows with AI-supported code review.
- Validating outputs prior to their use in finance reporting.
Hands-on Application
- Develop an AI-assisted Python workflow to analyze a sample finance dataset.
- Review generated code, test assumptions, and refine outputs through human validation.
Advanced Financial Data Analysis with AI
Financial Data Preparation and Quality
- Cleaning, validating, and standardizing finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date-related issues.
- Integrating data from multiple finance sources for comprehensive analysis.
Advanced 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
- Using AI to investigate movements, patterns, and unusual transactions.
- Formulating analytical questions and hypotheses based on finance data.
- Distinguishing valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Analysis
- Examining historical trends, drivers, and assumptions for forecasting purposes.
- Conducting what-if and sensitivity analysis to support finance decisions.
- Utilizing AI to support scenario narratives while maintaining financial controls.
Hands-on Application
- Execute 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.
AI-Based Financial Dashboards and Management Insights
Finance Dashboard Design
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards centered on decision-making questions rather than mere visual volume.
- Structuring views for executive, management, and analyst audiences.
Building Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Creating KPI cards, trends, variance visuals, drill-downs, and filters.
- Developing views for budget-versus-actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Exploring financial data through natural-language querying.
- Generating AI-assisted summaries and explanations of KPI fluctuations.
- Employing AI to identify areas requiring deeper analysis.
Dashboard Controls and Reliability
- Considering data refresh, traceability, validation, and reconciliation needs.
- Managing access, sensitive financial information, and controlled distribution.
- Preventing misleading visual or AI-generated conclusions.
Hands-on Application
- Construct an interactive financial dashboard using a structured dataset.
- Incorporate AI-supported management commentary linked to measurable financial movements.
Advanced AI Tools for General Ledger and Finance Operations
AI Applications in the General Ledger
- 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 Reconciliations
- Matching records and identifying exceptions across finance datasets.
- Assisting with 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 close tasks and conducting exception-based reviews.
- Using AI to assist with variance explanations, commentary, and review notes.
- Implementing structured approval and validation processes before final reporting.
Hands-on Application
- Analyze a sample GL dataset to identify anomalies and reconciliation exceptions.
- Produce 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 through goals, planning, tools, memory, actions, and feedback loops.
- Identifying where agentic AI supports finance operations and where human approval remains critical.
- Differentiating between single-agent and 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.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Implementing workflows for automated variance investigation and management commentary.
- Using agentic AI for GL exception triage, reconciliation support, and close-status monitoring.
- Leveraging agents for forecast refresh, scenario preparation, and finance query assistance.
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 within 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 fundamental understanding of finance, accounting, financial reporting, or FP&A concepts.
- Familiarity with Excel and experience working with financial datasets.
- No prior Python programming experience is required, though basic exposure to data analysis is advantageous.
- Basic awareness of AI or generative AI tools such as ChatGPT, Microsoft Copilot, or Claude is beneficial but not mandatory.
- Participants should be comfortable handling financial reports, KPIs, budgets, variances, and related finance data.
- A laptop equipped with access to the required training tools, datasets, and approved AI platforms is necessary for hands-on sessions.
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