How to effectively learn AI Prompting, with the 'AI for Financial Analysts (Prompt Course)'?

Build Your AI Co-Analyst: Practical Skills for Financial Analysts

AI for Financial Analysts (Prompt Course) gives you a complete, practical system for using AI in day-to-day finance work. It brings together forecasting, market research, portfolio analysis, budgeting, reporting automation, data visualization, regulatory review, M&A analysis, financial modeling, cash flow analysis, macroeconomic review, real estate analysis, process automation, tax planning, and financial training-so you can streamline workflows, improve consistency, and present findings with clarity.

The course focuses on repeatable prompt workflows that fit real analyst tasks. You'll learn how to brief AI tools clearly, feed them the right data, ask for structured outputs, and build checks that keep quality high. Everything is framed for practical use in corporate finance, FP&A, equity and credit analysis, treasury, M&A, real estate, tax, and compliance teams.

What you will learn

• Set up AI as a reliable assistant for core finance tasks: forecasting, scenario planning, budgeting, reporting, and valuations.
• Run market and competitive analysis workflows that synthesize filings, research notes, news, and macro indicators into concise insights.
• Structure portfolio reviews with risk/return summaries, performance attribution, scenario stress tests, and monitoring routines.
• Automate recurring reports with consistent formats, source references, and narrative explanations that align with your templates.
• Turn raw tables into audience-ready charts and narratives, including specification requests for dashboards and visual standards.
• Assess regulatory topics, identify potential compliance gaps, and document reasoning with clear, auditable outputs.
• Evaluate M&A candidates with summarized diligence notes, synergy framing, and valuation comparisons.
• Build and critique financial models, request sensitivity tables, and capture key assumptions for review.
• Map cash conversion cycles, working capital drivers, and liquidity scenarios with structured outputs you can drop into spreadsheets.
• Translate macro and sector indicators into focused business implications and monitoring routines.
• Summarize real estate market factors, deal comps, and underwriting considerations.
• Automate repetitive finance processes while keeping audit trails, controls, and documentation intact.
• Outline tax planning options, constraints, and documentation needs with clear caveats and references.
• Create training aids and checklists that help colleagues use AI responsibly in finance tasks.

How the course is organized

Each segment addresses a major finance function and shows how to set objectives, feed data, structure a prompt workflow, verify outputs, and package results for stakeholders. The modules build on each other so that by the end you can combine them into a cohesive system-moving from data gathering to analysis to reporting, all with consistent quality controls.

How the prompts work together

• Problem framing: Define the objective, audience, decision context, metrics, time horizon, constraints, and acceptable error ranges. This keeps the AI focused on what matters for the task at hand.
• Data intake and preparation: Provide clean inputs (tables, CSVs, links, text) and ask for data quality checks, assumptions, and gaps. You'll learn routines for summarizing and validating inputs before analysis begins.
• Analytical steps: Break big tasks into stages: baseline result, scenario variations, sensitivity passes, and commentary on drivers. This supports transparent reasoning you can audit.
• Structured outputs: Request tables, bullet lists, or JSON for easy handoff to spreadsheets, BI tools, and docs. You'll learn consistent formatting expectations so outputs can be reused.
• Review loops: Include reasonableness checks, comparisons with benchmarks or prior periods, and explicit flags for uncertainty or missing data.
• Packaging and communication: Convert findings into concise memos, slide-ready bullet points, or dashboard specifications with clear next steps.
• Governance: Keep prompts and outputs versioned, cite sources, and maintain an audit trail for compliance and peer review.

Effective use of AI prompts in finance

• Give context: State the business model, unit economics, and decision timeline. Specify region, currency, and reporting standards.
• Be explicit about criteria: Define metrics and thresholds (for example, accuracy targets for forecasts or limits for tracking error).
• Feed clean data: Provide labeled columns, consistent units, and timeframes. Ask the AI to confirm shapes, units, and time alignment.
• Request assumptions and sources: Have the model list assumptions, cite sources when applicable, and separate facts from estimates.
• Use scenarios and sensitivities: Ask for multiple cases and driver tables so you can see ranges, not just a single outcome.
• Demand structured outputs: Specify formats like JSON, CSV-ready tables, or numbered bullet points to simplify downstream use.
• Benchmark and back-test: Compare outputs to historical periods, analyst consensus, plan targets, or your own baselines.
• Control hallucinations: Require the model to state uncertainty, request clarification when data is missing, and avoid unsupported claims.
• Modularize prompts: Keep reusable blocks for data prep, analysis, and reporting. Mix and match modules across finance tasks.
• Version and document: Save prompt versions, datasets, outputs, and change notes to support audit and replication.

Data privacy, compliance, and ethics

• Confidentiality: Use approved environments. Apply data minimization, masking, or anonymization where required.
• Regulatory fit: Include disclaimers where needed, avoid investment advice wording if restricted, and ensure recordkeeping aligns with policy.
• Auditability: Keep logs of prompts, inputs, and outputs. Ask for citations and rationales that can be reviewed by compliance.
• Bias and fairness: Request checks for bias in datasets and assumptions, and note limitations in the final output.

Tooling and workflow integration

The course shows how to structure prompts so they play well with the tools you already use. You'll see how to:

• Move data between spreadsheets/BI tools and AI outputs using consistent schemas.
• Create dashboards from AI-generated chart specs and narratives.
• Set up retrieval workflows so the model references your approved documents.
• Use templates that convert model outputs into board-ready slides and management reports.

Who this course serves

• FP&A analysts and managers who need faster forecasting, budgeting, and reporting cycles.
• Equity and credit analysts who synthesize large volumes of information into decision material.
• Treasury, risk, and portfolio teams seeking consistent analysis and scenario testing.
• Corporate development and M&A specialists who want repeatable diligence and valuation routines.
• Controllers, finance ops, and tax teams focused on process automation and policy alignment.
• Real estate analysts who want structured market summaries and underwriting aids.

How the modules reinforce each other

Forecasting provides the baseline for budgets and cash flow planning. Market research and economic trend analysis shape assumptions that flow into models and valuation work. Portfolio analysis and risk reviews inform scenario design and stress tests. Reporting automation and visualization convert technical outputs into materials stakeholders can act on. Compliance reviews and audit trails give you confidence to use these workflows in production. Process automation ties it together into weekly and monthly routines you can run with minimal effort.

Quality and risk controls built in

• Assumption hygiene: Every analysis includes named assumptions, sources, and limitations.
• Cross-checks: Results are compared to baselines, peers, or prior periods, with variance explanations.
• Structured review: Prompts include review questions the model must answer before finalizing outputs.
• Traceability: All steps are documented so another analyst can reproduce the results.

Outcomes you can expect

• Shorter cycle times for forecasts, budgets, and monthly reporting.
• Clear, source-linked research notes you can trust and update quickly.
• Reusable templates that keep analyses consistent across teams.
• Stakeholder-ready narratives and visuals produced from the same core analysis.
• Better prioritization by automating routine tasks and focusing human effort on judgment calls.

Learning approach

The course emphasizes hands-on application. You'll work through each area with guidance on goals, inputs, review steps, and outputs that fit within your team's standards. The content is practical and repeatable, so you can plug these routines into live projects and refine them as your data and objectives evolve.

Prerequisites and time commitment

• Comfort with basic finance concepts, spreadsheets, and standard reports.
• No coding required; optional sections offer ideas for those who use SQL or Python.
• Flexible pacing: apply modules directly to your current workload for faster adoption.

Why this course matters

Finance teams are judged on clarity, consistency, and speed. This course helps you set up AI to meet those expectations with workflows that are transparent and reviewable. The result is a practical co-analyst that reduces repetitive effort, improves the quality of outputs, and supports better decisions-without sacrificing control or compliance.

Next step

Start with the area that matches your immediate goals-many learners begin with forecasting or reporting automation to see quick wins-then expand into research, modeling, compliance, and process automation. By the end, you'll have a cohesive set of AI workflows that work together across your finance responsibilities.