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Duration 35 hours
Course Outline
AI Fundamentals and AI-Assisted 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 within finance.
- Examining 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: Leveraging AI as a Coding Partner
- Introduction to Python for finance professionals, covering variables, data types, conditions, functions, and notebooks.
- Utilising AI assistants to generate, explain, debug, and refine Python code, fostering a collaborative approach rather than isolated coding.
- Mastering prompting techniques to ensure reliable, finance-focused code generation.
Handling Financial Data in Python
- Importing Excel and CSV data using Pandas and DataFrames.
- Executing filtering, grouping, aggregation, and calculation of key finance metrics.
- Leveraging AI to interpret errors, optimise logic, and document analysis procedures.
Practical Finance Coding Applications
- Automating repetitive calculations, variance analysis, and ratio analysis.
- Developing reusable Python workflows with AI-supported code review.
- Validating outputs to ensure accuracy before incorporating them into finance reporting.
Practical Application
- Constructing an AI-assisted Python workflow to analyse 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 standardising finance data.
- Addressing missing values, duplicates, inconsistent classifications, and date discrepancies.
- Merging data from multiple finance sources for comprehensive analysis.
Sophisticated Financial Analysis
- Anysising revenue, costs, margins, profitability, and working capital.
- Conducting budget versus actual, variance, and period-over-period analyses.
- Performing drill-down analysis to pinpoint critical financial drivers.
AI-Assisted Analysis and Anomaly Detection
- Employing AI to investigate movements, patterns, and irregular transactions.
- Formulating analytical questions and hypotheses based on finance data.
- Differentiating valuable signals from misleading AI-generated interpretations.
Forecasting and Scenario Modelling
- Utilising historical trends, drivers, and assumptions for forecasting purposes.
- Executing what-if and sensitivity analyses to support finance decision-making.
- Applying AI to aid in scenario narratives while maintaining financial controls.
Practical Application
- Conducting end-to-end analysis of a finance dataset to identify key variances and anomalies.
- Drafting a concise, AI-assisted finance insight summary backed by underlying data.
AI-Driven Financial Dashboards and Management Insights
Finance Dashboard Design Principles
- Selecting meaningful KPIs for finance, management, and operational reporting.
- Designing dashboards centred on decision-making questions rather than visual clutter.
- Structuring views for executive, management, and analyst audiences.
Constructing Interactive Financial Dashboards
- Connecting and transforming finance data for dashboard integration.
- Creating KPI cards, trend lines, variance visuals, drill-downs, and filters.
- Developing views for budget versus actual, profitability, cash flow, and performance metrics.
AI-Enhanced Dashboarding
- Utilising natural-language queries to explore financial data.
- Generating AI-assisted summaries and explanations for KPI movements.
- Using AI to highlight areas requiring deeper analytical attention.
Dashboard Integrity and Reliability
- Considering data refresh, traceability, validation, and reconciliation.
- Managing access rights, sensitive financial information, and controlled distribution.
- Avoiding misleading visualisations or AI-generated conclusions.
Practical Application
- Building an interactive financial dashboard from a structured dataset.
- Incorporating AI-supported management commentary linked to measurable financial changes.
Advanced AI Tools for General Ledger and Finance Operations
AI Applications in the General Ledger
- Analysing GL accounts, transaction patterns, and posting behaviours.
- Using AI to support transaction classification and account-level reviews.
- Detecting unusual, high-risk, or out-of-pattern entries.
AI in Reconciliations
- Matching records and identifying exceptions across various finance datasets.
- Supporting bank, intercompany, and balance-sheet reconciliations.
- Prioritising unreconciled items for human investigation.
Journal Entry Analytics
- Detecting duplicate, unusual, or manual journal entries.
- Analysing period-end journals and generating supporting explanations.
- Establishing risk indicators and review checkpoints for finance teams.
AI in Financial Close and Reporting
- Prioritising 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 Application
- Analysing 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 in a Finance Context
- Defining the characteristics of agentic AI workflows, including 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.
- Integrating agents with structured finance data and approved tools.
- Designing escalation rules, checkpoints, and approval boundaries.
Agentic Use Cases in Finance
- Automating variance investigations and generating management commentary workflows.
- Facilitating GL exception triage, reconciliation support, and close-status monitoring.
- Enabling forecast refreshes, 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.
- Establishing safe operating boundaries prior to production deployment.
Final Practical Capstone
- Integrating Python with AI, advanced analytics, and dashboard outputs into a single finance use case.
- Designing an agentic workflow that analyses results, flags exceptions, and prepares management insights.
- Presenting the workflow, controls, outputs, and recommended next steps
Requirements
- Fundamental knowledge 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.
- Basic familiarity with AI or generative AI tools, such as ChatGPT, Microsoft Copilot, or Claude, is recommended but not mandatory.
- Comfort working with 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.
Testimonials (2)
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