Financial Analysis Using Artificial Intelligence Training Course

Participants can explain how common AI methods support and constrain financial analysis.

• Compare generative AI, machine learning and rule-based automation
• Explain tokens, context windows, temperature and probabilistic outputs
• Recognise hallucinations, stale knowledge and unsupported financial claims
• Use retrieval-augmented generation for controlled document-based analysis
• Match AI techniques to forecasting, classification and anomaly tasks
• Document model purpose, users, inputs, outputs and decision impact

Participants can prepare finance data for AI use and apply proportionate governance controls.

• Profile missing values, duplicates, outliers and inconsistent account mappings
• Reconcile source totals before submitting data for analytical processing
• Classify confidential, personal, market-sensitive and restricted finance information
• Apply data minimisation, masking and approved-platform requirements
• Establish human review points for material financial conclusions
• Maintain prompt, source, output and reviewer audit trails

Participants can write controlled prompts that produce usable financial analysis outputs.

• Specify role, task, source scope, assumptions and output format
• Use structured prompts for ratio analysis and variance explanations
• Require citations, calculations, caveats and missing-data disclosures
• Break complex analysis into sequenced, reviewable prompt stages
• Apply few-shot examples to standardise management-reporting language
• Test prompts against known answers and boundary cases

Participants can analyse financial statements and validate AI-generated observations.

• Recast income statements into revenue, margin, overhead and profit drivers
• Perform common-size analysis for income statements and balance sheets
• Calculate liquidity, profitability, efficiency and solvency ratio families
• Compare multi-period trends using indexed and percentage-change analysis
• Identify accounting-policy effects that distort period comparisons
• Validate AI-generated observations against published financial statement notes

Participants can diagnose cash-generation issues using working-capital and cash-flow analysis.

• Trace operating cash flow from profit through non-cash adjustments
• Calculate receivables, inventory and payables days from financial data
• Build cash conversion cycle analysis using operating-driver assumptions
• Investigate liquidity pressure through ageing, payment terms and seasonality
• Use AI to summarise debtor, supplier and inventory exception reports
• Prioritise working-capital actions by cash impact and implementation feasibility

Participants can investigate material variances and distinguish drivers from symptoms.

• Decompose revenue variance into volume, price, mix and timing
• Separate fixed-cost, variable-cost and efficiency variance components
• Apply waterfall charts to communicate bridge analyses clearly
• Use drill-down questions to investigate unexpected performance movements
• Generate variance hypotheses from management accounts and operational data
• Rank findings by materiality, controllability and required management action

Participants can create a reusable financial analysis brief for a live business question.

• Define decision owner, decision date and required recommendation
• Set baseline periods, peer comparators and analytical materiality thresholds
• List source documents, datasets and unresolved data limitations
• Formulate driver hypotheses requiring financial and operational testing
• Create an evidence register linking claims to source calculations
• Draft review criteria for accuracy, relevance and decision usefulness

Participants can build a driver-based forecast using financial and operational assumptions.

• Select volume, price, capacity and productivity forecast drivers
• Construct revenue forecasts from units, prices and commercial assumptions
• Model cost behaviour using fixed, variable and step-cost categories
• Link headcount plans to payroll, benefits and recruitment timing
• Incorporate seasonality, contract milestones and phasing assumptions
• Use AI to identify forecast assumptions requiring management confirmation

Participants can assess forecast quality and improve assumptions through structured review.

• Measure forecast error using variance, MAPE and bias indicators
• Compare forecast versions through assumption and outcome tracking
• Identify optimism bias, anchoring and management override patterns
• Apply rolling forecasts to refresh near-term financial expectations
• Set trigger thresholds for forecast re-estimation and escalation
• Document assumption changes with owners, rationale and evidence

Participants can quantify alternative financial outcomes and communicate key uncertainties.

• Develop base, upside and downside scenarios from named assumptions
• Apply one-way and two-way sensitivity analysis to key drivers
• Use scenario matrices for price, volume, cost and timing changes
• Calculate break-even points under alternative contribution-margin assumptions
• Identify variables with highest impact on profit and cash
• Write management narratives distinguishing risks, uncertainties and decisions

Participants can evaluate investment proposals using cash-flow-based appraisal methods.

• Define incremental cash flows, sunk costs and opportunity costs
• Calculate net present value using stated discount-rate assumptions
• Interpret internal rate of return and modified internal rate
• Apply payback and discounted payback for liquidity-focused decisions
• Model tax, inflation, residual value and working-capital effects
• Use AI to challenge investment-case assumptions and missing risks

Participants can prepare and test core valuation inputs for strategic decisions.

• Build discounted cash flow forecasts from operating-driver assumptions
• Estimate terminal value using growth and exit-multiple approaches
• Assess weighted average cost of capital input components
• Compare enterprise value, equity value and net-debt bridge calculations
• Use trading multiples with relevant peer-selection criteria
• Challenge valuation narratives against cash flow and risk evidence

Participants can use AI to structure external research without treating generated content as evidence.

• Define research questions linked to revenue, margin and investment drivers
• Extract competitor metrics from annual reports and investor presentations
• Compare market claims against primary sources and dated evidence
• Create competitor scorecards for growth, margin, scale and risk
• Use PESTLE and Porter's Five Forces for strategic assumptions
• Record source limitations, conflicting evidence and unresolved questions

Participants can identify unusual transactions and specify investigation priorities.

• Define expected transaction patterns using historical and business-rule evidence
• Apply Benford's Law as an initial digit-pattern screening method
• Detect duplicate payments, unusual vendors and round-number transactions
• Use z-scores to flag statistically unusual account movements
• Separate legitimate exceptions from indicators requiring investigation
• Document alert logic, reviewer actions and final disposition

Participants can produce concise management reporting from validated financial analysis.

• Structure reports around performance, drivers, risks and decisions
• Select charts matching comparison, composition, trend and distribution questions
• Write variance commentary using fact, cause, consequence and action
• Use AI to draft narratives from verified calculations only
• Distinguish reported results, forecasts, scenarios and management targets
• Build executive summaries with explicit recommendations and decision requests

Participants can review AI-generated analysis using a documented quality-assurance protocol.

• Check arithmetic, sign conventions, units and period consistency
• Trace claims to source data, calculations and approved assumptions
• Identify fabricated citations, unsupported comparisons and omitted caveats
• Challenge causal claims using alternative explanations and evidence
• Apply materiality-based review depth to financial outputs
• Record corrections, reviewer comments and final approval decisions

Participants can plan a controlled AI implementation for a finance process.

• Map current finance workflows, bottlenecks, handoffs and control points
• Prioritise use cases by value, feasibility, risk and data readiness
• Define process owners, reviewers, escalation paths and acceptance criteria
• Establish performance measures for time saved, accuracy and adoption
• Create prompt libraries and approved output templates
• Plan pilot scope, user training and post-implementation review

Participants can defend financial recommendations when assumptions and evidence are challenged.

• Frame recommendations with decision, financial impact, evidence and conditions
• Anticipate executive questions on assumptions, downside and opportunity cost
• Present sensitivity results without overstating forecast precision
• Explain AI use, controls and reviewer accountability transparently
• Handle conflicting stakeholder views through evidence-based challenge
• Agree follow-up actions, owners, dates and monitoring indicators

Participants can build, review and present a complete AI-assisted financial analysis and recommendation for a strategic business decision.

• Define a strategic decision and assemble relevant financial evidence
• Analyse historical performance, cash flow and key value drivers
• Build a driver-based forecast with documented assumptions and scenarios
• Evaluate investment, valuation or performance-improvement options using financial methods
• Review AI-generated outputs against sources, calculations and governance controls
• Present a management recommendation with risks, actions and decision requests