Indian Institute of Technology Kanpur

Financial Modelling with AI

Indian Institute of Technology Kanpur

Artificial Intelligence & Machine Learning

Application Deadline Approaching

Last date to apply: November 20, 2026 · Program starts on November 22, 2026

About Programme

Become an AI-assisted financial modeller. Master 3-statement models, DCF, LBO, and M&A, built and validated with AI copilots.
Hands-on with the modern finance AI stack. ChatGPT, Claude, Microsoft Copilot, Advanced Excel, Power BI, & Python.
Learn to audit what AI gets wrong. Dedicated module on catching AI-generated errors before they reach a client.
Built for working finance professionals: 3 months, weekend live classes, 26 sessions plus recorded access.

What you'll learn

Build 3-statement models by linking income statements, balance sheets, & cash flow statements.
Use AI tools to accelerate analysis, verify outputs, and avoid financial modelling errors.
Build AI-assisted DCF valuations, test assumptions, and validate results against manual benchmarks.
Apply modelling skills to LBOs, M&A analysis, FP&A forecasting, and scenario planning.
Turn financial data into insights through earnings models, credit analysis, dashboards, & commentary.
Audit AI outputs, identify bias and risk, and apply responsible AI governance practices.

Skills you'll gain

Three-Statement Financial ModellingAI-Assisted Financial AnalysisDCF Valuation & Financial ForecastingLBO & M&A ModellingFP&A & Scenario PlanningFinancial Model Auditing & ValidationAI Governance & Risk AwarenessCredit & Risk AnalysisFinancial Model Auditing

Programme Content

Financial Statement Fundamentals

  • Reading and interpreting the Income Statement, Balance Sheet, and Cash Flow Statement
  • Ratio analysis: profitability, liquidity, solvency
  • Linking the three statements: circularity, working capital, depreciation schedules
  • Common modelling errors and how to spot them
  • Case Study: Diagnosing financial health from a real public company's filings

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    Core Financial Modelling Principles

  • Building a 3-statement model from scratch
  • Assumptions, drivers, and forecasting logic
  • Zero-based budgeting and variance analysis
  • Modelling best practices: structure, formatting, version control
  • Mini-Project: Build a fully linked 3-statement model template

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    AI Fundamentals for Finance Professionals

  • How LLMs (ChatGPT, Claude, Gemini) actually work, strengths and failure modes
  • Prompt engineering for financial research and structured outputs
  • Where AI helps vs. where it still fails (accuracy, hallucination risk in numbers)
  • Data privacy and confidentiality considerations with financial data
  • Case Study: Where AI-generated financial analysis went wrong

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    AI-Assisted Valuation & DCF Modelling

  • AI-assisted fundamental analysis: extracting data from annual reports/filings
  • Building a DCF with AI-generated assumptions
  • Comparable company analysis and precedent transactions
  • Cross-checking AI-generated valuation outputs against manual benchmarks
  • Mini-Project: End-to-end company valuation using 3 methodologies

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    AI for Investment Banking & M&A Modelling

  • LBO modelling with AI-accelerated workflows
  • M&A accretion/dilution analysis
  • AI-assisted deal screening and due-diligence support
  • Building IB-ready pitch materials faster with AI
  • Case Study: An AI-accelerated LBO teardown

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    AI-Powered FP&A & Forecasting

  • Driver-based budgeting and rolling forecasts with AI
  • Supervised learning/ML techniques for forecasting key business metrics
  • Variance analysis automation
  • AI-assisted scenario planning and CFO-ready commentary

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    Agentic AI & Multi-Step Financial Workflows

  • Building automated pipelines (data pull → model build → sensitivity run) with human review
  • Comparative practice: ChatGPT vs. Claude vs. Copilot choosing the right tool for the task
  • Demonstration of Microsoft Copilot Agent Mode, Claude, and Python-based automation
  • Executive Activity: Design an agentic workflow for a recurring FP&A reporting task
  • Advanced Microsoft Excel for financial modelling: structured models, dynamic formulas, scenario analysis, sensitivity tables, dashboards, and error checks.

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    AI for Equity Research & Credit/Risk Analysis

  • Earnings model building and AI-assisted estimate revision
  • Sentiment analysis on earnings calls and filings (NLP for finance)
  • AI-assisted credit scoring and risk-flagging fundamentals
  • Portfolio-level analytics with AI support
  • Case Study: Credit-risk model evaluation with a lending recommendation

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    AI Tools Deep Dive for Financial Dashboards

  • Power BI and Python (pandas, scikit-learn) for financial dashboards
  • AI-assisted data visualization and CFO-ready reporting
  • Demonstration of ChatGPT Code Interpreter, Power BI Copilot, and Python automation
  • Executive Activity: Build and present an AI-assisted financial dashboard for a business case

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    AI Model Auditing & Validation

  • Techniques to catch AI-generated errors before they reach a client/manager
  • Building a validation checklist for AI-assisted models
  • Model-risk thinking: what to trust, what to re-check
  • Exercise: Audit a peer's AI-assisted model and document findings

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    AI Governance, Ethics & Risk in Finance

  • AI governance frameworks for BFSI (RBI/SEBI guidance, EU AI Act "high-risk" classification)
  • Model risk management adapted for AI
  • Bias and fairness considerations in AI-assisted lending/credit decisions
  • Regulatory environment and audit-readiness for AI-assisted financial work
  • Case Study: Explainability failure in an AI credit-decisioning system

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    Capstone Project & Presentations

  • Building an end-to-end AI-assisted financial model (3-statement, DCF, or LBO learner's choice)
  • Written defense of assumptions and an AI-audit log
  • Role-specific portfolio angle: IB/FP&A/Equity Research track
  • Final presentations & industry/faculty feedback

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    Financial Modelling with AI | IIT Kanpur