Glossary of AI Terminology

Artificial Intelligence (AI) is a rapidly evolving field filled with complex concepts and jargon. To help you navigate this landscape, we’ve put together a simple guide to some of the key terms you’ll encounter.

A

Artificial Intelligence

The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.  

Algorithm

A set of rules or procedures for solving a problem.

Automation

The use of technology to perform tasks without human intervention.

AI ethics

The study of ethical and societal implications of artificial intelligence.

B

Bias

A systematic error in a model that can lead to unfair outcomes.

C

Chatbot

A computer program designed to stimulate conversation with a human user.

D

Data Analytics

The process of examining large datasets to uncover patterns, trends, and insights.

Data Integration

The process of combining data from multiple sources into a unified view.

Deep Learning

A subfield of machine learning that uses artificial neural networks with multiple layers to learn from data.

E

Ethics & Compliance

The principles and regulations governing the development and use of AI, ensuring fairness, accountability, and transparency.

M

Machine Learning (ML)

A subset of AI that allows systems to learn and improve from experience without being explicitly programmed. 

Model

A mathematical representation of a real-world system.

N

Natural Language Processing (NLP)

The ability of computers to understand, interpret, and generate human language.

Natural Language Generation (NLG)

The ability of computers to generate human-like text in response to a wide range of prompts and situations.

Neural Networks

Computing systems inspired by the human brain, capable of learning and making decisions.

O

Overfitting

When a model is too complex and performs well on training data but poorly on new data.

P

Predictive Analytics

Using data to predict future outcomes.

Prescriptive Analytics

Using data to recommend actions to optimise outcomes.

R

Recommendation System

A system that suggests items or content to users based on their preferences.

Reinforcement Learning

A type of machine learning that allows an agent to learn by trial and error in an interactive environment.

S

Sentiment Analysis

Determining the emotional tone of text data.

Social Listening

Monitoring online conversations to understand public opinion.

Standardisation

The process of establishing and implementing agreed-upon norms and technical specifications.

U

Underfitting

When a model is too simple and cannot capture the underlying patterns in the data.

AI in Mortgage Brokering

• Automated Valuation Model (AVM): An AI-driven system that estimates property values based on market data and trends.
• Automated Underwriting: AI systems that assess mortgage applications and make lending decisions based on predefined criteria.
• Document Processing: AI-powered tools that can extract and analyse information from mortgage-related documents.
• Client Profiling: Using AI to analyse client data and create personalised mortgage recommendations.
• Fraud Detection: AI systems that can identify potential fraudulent activities in mortgage applications.

AI Tools 

• ChatGPT: A powerful language model that can assist with content creation, client communication, and market research.
• Copy.ai: An AI tool for creating marketing content, including website copy and social media posts.
• Grammarly: An AI-powered writing assistant that helps improve the quality of written communication.
• pdf.ai: A tool that allows brokers to quickly extract and understand key information from large documents, such as lender terms and conditions.
• Fireflies.ai: An AI-powered tool for transcribing, summarising, and analysing client meetings and calls.

For more insights on how to integrate AI into your company, check out our A Brokers Guide to AI guide and The Best AI Tools for Mortgage Brokers.