This module builds the right foundation by helping finance professionals understand exactly what AI is, how it differs from automation, and how it fits into a finance workflow. It simplifies complex terms like Machine Learning and Deep Learning using finance-relevant analogies. You’ll learn how to think in prompts, what limitations to watch for, and how AI can be treated like your junior analyst. The module ends with a quiz and an engaging activity that challenges you to spot AI already at work in your daily environment.

Most finance teams live inside Excel and PowerPoint. This module shows you how to use AI inside those very tools. Learn how to query Excel using plain English, build dynamic dashboards with AI, and generate entire business presentations using platforms like Gamma and Napkin. You’ll also work on a caselet based on actual board-level presentations and Indian CFO dashboards. By the end of this module, you’ll be able to create a full financial deck from scratch using raw data and AI tools.

AI is only as good as the data you give it. In this module, you will learn how to prepare, clean, and structure financial data to get meaningful results from AI tools. You’ll work with both structured formats like transaction logs and unstructured formats like invoices. You’ll explore tools such as NotebookLM, Claude, and Gemini to understand how to manage messy data, extract usable insights, and make it AI-ready. You’ll complete hands-on activities like analyzing transaction logs and automating invoice data interpretation.

This is where GPT becomes your financial forecaster. Learn how to generate revenue and cash flow forecasts using AI. Use GPT to score credit risk and simulate business scenarios. Through caselets like Coca-Cola and Indian D2C brands, you’ll understand how AI supports strategic planning. You’ll then build your own forecasting model using historical data and test its accuracy against known trends. The goal is to move beyond Excel formulas and start making intelligent financial predictions using AI logic.

Finance professionals deal with enormous volumes of transactions. This module teaches you how to train AI to catch what the human eye may miss. Learn how to identify unusual patterns, fake entries, and outliers in financial records. You’ll study actual implementations from HDFC Bank and Mastercard to understand how AI systems are already used in real financial environments. Through hands-on work in Excel and Sheets, you’ll build a basic anomaly detection system tailored for your own use case.

This module helps you master one of the most valuable applications of AI in finance: transforming dense information into clear communication. Learn how to use ChatGPT and other LLMs to summarize annual reports, extract insights from earnings calls, and perform sentiment analysis on investor updates or budget documents. Study examples from JPMorgan and Zerodha. You’ll build your own AI workflow that takes raw policy documents or Screener data and converts them into concise summaries or investment pitches.

Here you will learn how to build risk detection systems that work in real-time. Create rule-based workflows using GPT to simulate underwriting logic, generate red flags, and design early warning systems. Study caselets from fintech leaders like Razorpay and Stripe Radar to understand how AI models are trained for risk behavior. Then, using tools like Zapier and Sheets, you’ll build a functioning workflow that mirrors how modern finance teams automate their risk checks and alerts.

This module takes you inside the architecture of finance-specific chatbots like Eva by HDFC and Erica by Bank of America. You’ll learn the role AI plays in managing repetitive client queries, automating invoice responses, and delivering financial summaries. The hands-on project includes building a basic chatbot workflow using no-code tools that can be applied to any finance function such as collections, KYC follow-ups, or investor queries. No programming background is required.

In this module, you shift gears from using AI to designing with AI. Explore prompt engineering, audit trails, model bias, and ethical use cases in financial decision-making. Understand how top firms like KPMG and BlackRock are deploying AI responsibly through tools like Copilot and Aladdin. You’ll also experiment with no-code tools like Replit, Zapier, and NotebookLM to design and automate real finance workflows. The module ends with a challenge to build an AI solution relevant to your team or business unit.

This is where everything comes together. You will apply everything you’ve learned to solve a real financial problem end-to-end using AI. From cleaning the data to prompting the model, from generating insights to presenting outcomes, this final project is your opportunity to build something tangible and functional. It is also your final evaluation before receiving certification from The WallStreet School, validating your ability to apply AI practically and confidently in any finance role.