Contents
What do financial institutions need to understand when it comes to AI?
Chapter I
What are the potential benefits and use cases of AI for banks?
Chapter II
How do you mitigate risk when it comes to AI?
Chapter III
How do you get started?
Chapter IV
What does AI look like in banking?
Chapter V
01
What do financial institutions need to understand when it comes to AI?
Chapter I
What do financial institutions need to understand when it comes to AI?
Chapter I
What are the potential benefits and use cases of AI for banks?
Chapter II
How do you mitigate risk when it comes to AI?
Chapter III
How do you get started?
Chapter IV
What does AI look like in banking?
Chapter V
02
What do financial institutions need to understand when it comes to AI?
Chapter I
What are the potential benefits and use cases of AI for banks?
Chapter II
How do you mitigate risk when it comes to AI?
How do you get started?
What does AI look like in banking?
Chapter III
Chapter IV
Chapter V
Introduction
You’ve seen it everywhere—the news, industry trades, and across social media— how can we better understand AI and the ways it will impact our personal and professional lives? At this point, we’re no strangers to it.
This guide will take you through the broader context and macroenvironment within artificial intelligence (AI) and provide on-the-ground intel on the “why” and “how” when it comes to leveraging the value of AI in banking.
03
AI has changed over time.
With 63% of companies prioritizing AI over other digital tech initiatives, we’re seeing the financial services industry highlight the significance of data, value, and technology fluency as the backbone of investment. The middle market can benefit from considering these trends and how they can streamline operations.
How did we get to this point?
63%
What do financial institutions need to understand when it comes to AI?
Chapter I
What are the potential benefits and use cases of AI for banks?
Chapter II
How do you mitigate risk when it comes to AI?
Chapter III
How do you get started?
Chapter IV
What does AI look like in banking?
Chapter V
04
What do financial institutions need to understand when it comes to AI?
Chapter I
What are the potential benefits and use cases of AI for banks?
Chapter II
How do you mitigate risk when it comes to AI?
Chapter III
How do you get started?
Chapter IV
What does AI look like in banking?
Chapter V
Traditional AI
Predefined rules
Relies on encoding of human knowledge bases and rules into computer systems but does not learn or adapt from data
Alan Turing
Coined the term "Artificial intelligence"
Machine Learning
Analysis and prediction
Algorithms and models train computers on large sets of data to execute tasks (classify an image, predict an output, etc.)
DeepBlue
Chess-playing computer beats reigning chess champion
Deep Learning
Complex tasks
Evolves ML by using ¨deeper¨ neural nets and model to mirror human learning with more complex data sets (Robotics, etc.)
Alexa
Voice-activated virtual assistant that performs tasks
Generative AI
Content generation
Uses deep learning techniques to create new content (images, video, text). Chat GPT is one example of this.
ChatGPT
A large language model is launched by OpenAI
Josephn Weizenbaum
Created ELIZA, an early natural language processing computer program while at MIT
IBM Watson
Watson beats the world's two best players at Jeopardy
BERT
Algorithm created to understand vague language in text by using context clues
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