AI for Financial Analysis and Reporting (SF) (Classroom & Asynchronous elearning))
About This Course
This course covers the application of AI to financial analysis, reporting and strategic decision support.
Participants learn where AI adds value across the finance cycle and how to prepare financial datasets for analysis, covering extraction from a cloud accounting system, account mapping, period alignment, multi-entity consolidation and verification discipline. They then investigate month-end variances across a consolidated P&L using general ledger data extracted from the accounting system, triage transaction anomalies against defined exception rules, and finally build a three-year driver-based forecast, benchmark performance against industry comparators and assemble a board reporting pack, with every AI-generated figure verified against source data and recorded in an audit-ready verification log.
The work is done in Claude as the primary tool for dataset analysis, dashboard building and commentary drafting, with Perplexity AI providing industry benchmarks and comparator data. Participants use the full Claude workspace: Projects to organise scenario datasets and policies, Artifacts to build interactive dashboards and the board reporting pack, Skills to standardise repeatable analysis workflows, Cowork and its plugins, especially the finance plugin, to run hands-on multi-file finance workflows end to end, and MCP connectors to ground analysis in enterprise finance data.
The course suits finance managers, accountants, financial analysts and business managers involved in financial planning. It supports progression into roles such as Financial Analyst, FP&A Analyst, Finance Business Partner, Management Accountant and Finance Manager.
What You'll Learn
Knowledge Statements
1. Explain how AI and analytics, delivered through the Claude workspace of Projects, Artifacts, Skills, Cowork plugins and MCP connectors, support financial analysis, reporting accuracy and strategic decision support.
2. Describe how Claude and the Cowork finance plugin can be applied to complex finance problems: variance investigation, transaction anomaly detection and driver-based forecasting.
3. Identify the benefits and limitations of AI-assisted analytics in financial reporting, and the data-governance obligations when financial data is accessed via MCP connectors.
Skills Statements
1. Analyse financial datasets extracted from a cloud accounting system into Claude, organised in Projects and accessed via MCP connectors, to generate accurate summaries, variance explanations and management commentary.
2. Develop interactive financial dashboards as Claude Artifacts to visualise performance indicators and detect anomalies, saving repeatable analyses as reusable Claude Skills.
3. Gather and normalise industry benchmarks using Perplexity AI to contextualise organisational performance, verifying every AI-generated figure against source data before it is reported.
Ability Statement
Apply the full Claude workspace, including Projects, Artifacts, Skills, the Cowork finance plugin and MCP connectors, to complex financial analysis and reporting problems and translate the findings into accurate, verifiable reports and recommendations.
Entry Requirements
Minimum O level or its equivalent
Basic knowledge of financial reporting and familiarity with data analysis tools.