Lesson 85 - Advanced Accounting Course

Lesson 85: Using Machine Learning for Financial Analysis

🧠 What Is Machine Learning?

Machine Learning (ML) is a type of Artificial Intelligence (AI) that allows computers to learn from data and make predictions or decisions without being explicitly programmed.

Imagine teaching a computer how to read financial data and then letting it spot trends, make forecasts, and even detect fraud — all on its own! 🚀

🔍 Why Use ML in Financial Analysis?

Machine Learning helps:

• 📈 Predict trends in revenue, expenses, or stock prices
  
  

• 🕵️‍♀️ Detect unusual activity (like fraud or errors)
  
  

• 🧾 Automate tasks like reading financial statements
  
  

• 💡 Find insights hidden in massive datasets
  
  

🧰 Common ML Techniques in Finance

🔮 ML in Action – Real Use Cases 

📊 Machine Learning vs. Traditional Analysis 

📘 Step-by-Step: How ML Is Used in Finance

1. 📥 Collect Data (e.g., past financial statements, transactions)
   
   

2. 🧹 Clean Data (remove duplicates, fill missing values)
   
   

3. 📊 Train the ML Model (teach the machine what to look for)
   
   

4. 🧪 Test the Model (see if it's accurate)
   
   

5. 🚀 Deploy the Model (let it run in real-time)
   
   

6. 🔄 Improve the Model (learn from results and update it)

🧠 Types of Machine Learning

✅ Benefits of ML in Finance 

🚧 Challenges of ML in Finance 

📌 Real-World Example

A financial institution uses ML to forecast loan defaults.
It trains a model using data from 5 years of customer history.
The model flags high-risk applicants before loans are approved, reducing losses by 25% in the first year. ✅

📘 Summary

• Machine Learning helps make finance smarter and faster 💡
  
  

• It finds patterns, predicts outcomes, and automates analysis 🔍
  
  

• Common techniques include regression, classification, and clustering 🧠
  
  

• Used across forecasting, fraud detection, investment, and risk 🚨
  
  

• Needs quality data, good design, and ethical use 🔐