AI, Machine Learning, and GenAI – What’s the Difference?

 

AI, Machine Learning, and GenAI – What’s the Difference?

Before diving into exploratory data analysis (EDA), it’s important to understand the broader AI landscape and where Machine Learning (ML) and Generative AI (GenAI) fit into financial risk modeling. Here is a brief run-down:

What is Artificial Intelligence (AI)?

AI refers to any system that can perform tasks requiring human intelligence, such as recognizing patterns, making decisions, or processing language. It is a broad category that includes both traditional rule-based systems and Machine Learning (ML) models that improve based on data.
 

What is Machine Learning (ML)?

Machine learning is a subset of AI that learns from historical data to predict future outcomes. In financial services, ML models are used for credit scoring, fraud detection, and risk assessment.

  • Example: A logistic regression model trained on customer repayment history to predict the likelihood of delinquency.
     

What is Generative AI (GenAI)?

GenAI is a specialized form of AI that can create new content, such as text, images, or structured code, based on training data. Unlike ML, which is designed for structured predictions, GenAI assists with automation, summarization, and data exploration—making it a valuable tool in EDA.

  • Example: Using GenAI to generate a summary of credit risk trends instead of manually coding an exploratory analysis.

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