chapter one

1 Introducing AI in finance

 

This chapter covers

  • Defining AI in finance and its importance
  • Outlining core AI uses: credit, fraud, and more
  • Exploring the four-layer AI approach
  • Presenting a credit scoring workflow
  • Preparing tools and infrastructure for AI solutions

In May 2026, JPMorgan Chase CEO Jamie Dimon said AI would likely reduce some jobs over time and that the bank would likely hire more AI specialists and fewer bankers in certain categories [1]—reflecting a profound shift in the skills and expertise the financial sector now demands.

Across Wall Street, this pattern repeats. Morgan Stanley, for example, has invested heavily in AI-powered research and advisory tools, enabling its analysts and advisors to deliver more personalized and data-driven insights than ever before.

These developments underscore a fundamental truth: AI is no longer a sideline curiosity in the world of finance. It has moved to the forefront, reshaping how credit decisions are made, how fraud is detected, and how investment strategies are formulated.

1.1 AI in the context of finance

1.2 The new imperative: Why AI is no longer optional

1.3 How AI is transforming finance

1.3.1 Managing risk and ensuring compliance

1.3.2 Extracting market intelligence for better investments

1.3.3 Enhancing customer experiences

1.3.4 Improving operational efficiency

1.4 Core building blocks of financial AI applications

1.4.1 Data asset layer

1.4.2 Modeling layer

1.4.3 Strategy and monitoring layer

1.4.4 Application layer

1.5 How the layers work together: A credit scoring example

1.5.1 The data asset layer in credit scoring

1.5.2 The modeling layer in credit scoring

1.5.3 The strategy and monitoring layer in credit scoring

1.5.4 The application layer in credit scoring

1.6 Tools, data, and infrastructure you’ll need

1.6.1 Programming language and frameworks

Summary