AI-powered tools aren’t the thing of science fiction films anymore; they’re a crucial integration in everyday business processes. There’s no ignoring that AI may soon become a core component of insurance business growth, and neglecting to implement AI technology is neglecting a key player in your brokerage’s success.

Insurance chatbots are and have been a common addition to many insurance brokerage websites. While these have been pivotal in the transition to stronger AI tools, they pale compared to other developments. We’ll get more into that a little later.

Stay on top of the shift by arming yourself with knowledge. Let’s begin with terminology.

Exploring AI Terminology

As AI technology evolves and develops, new terminology emerges. While your brokerage or agency doesn’t need to have every new gadget under the sun, being aware of the different terms and how they relate to insurance processes is key in understanding which AI tools to implement and which to not.

Here are 10 of the most essential AI terms:

AI Agent

“AI Agent” refers to an AI tool that is created to assist both customers and prospects with the likeness and knowledge of real (human!) insurance professionals. This can manifest in many different forms, such as insurance chatbots, interactive quoting forums, communication platforms, or otherwise.

Insurance Virtual Assistant

An “Insurance virtual assistant” is similar in function to an AI agent. Insurance virtual assistant is just another fancy way of referring to conversational AI software.

However, unlike an insurance chatbot, an insurance virtual assistant adapts and learns from its interactions with consumers, offering more comprehensive responses and providing solutions rather than scripted responses. An insurance virtual assistant is an insurance chatbot with infinitely more extensive capabilities.

Insurance Chatbot

Insurance chatbots have been the initial exposure to AI for many different brokerages and agencies. However, an insurance chatbot and an insurance virtual assistant are not one and the same.

While an insurance chatbot may certainly have its perks, its inherent purpose is to man websites to tackle basic, pre-programmed queries and provide scripted responses. It does not adapt to real-time conversations or stimulate contextualized interactions, but it can be good to offset some of the phone calls for basic issues and topics.

Conversational AI

The term conversational AI refers to software allowing machines to not only understand and process, but respond to human language in a way that is natural. Conversational AI can be used to power insurance virtual assistants (like Koios’s Olivo – more about that later) as well as voice recognition systems that can handle meaningful conversations.

NLP

NLP is short for Natural Language Processing, which is a brand of AI centred on computers’ ability to understand and interpret human language in a useful and meaningful way. NLP is helping bridge the gap between human communication and machine understanding by analyzing and processing natural language.

ML (Machine Learning)

ML is short for Machine Learning, a subset of AI that programs computers to learn and make predictions or decisions without explicit scripting. Rather than adhering to strict code, ML uses statistical models and algorithms to analyze and learn patterns from data, thereby improving your AI tools’ performance with time and experience.

Predictive Analytics

Think of predictive analytics sort of like a crystal ball, but instead of being powered by magic it’s powered by data and AI.

Predictive analytics uses algorithms, stats, and ML to identify patterns and predict what could happen next. For insurance and finance specifically, this means forecasting customer behaviour and market trends in order to stay ahead of the game. This is an incredibly useful aspect of AI that can help insurers make proactive, data-driven decisions that help improve pricing and enhance consumer satisfaction.

AI Stack

An “AI stack” is the layered presentation of different technologies and AI tools that work as partners to both develop and deploy systems.

An AI stack can combine multiple components such as data collection, ML frameworks, deployment platforms, model training, and user interfaces. For insurance professionals, an AI stack may involve conversational AI for customer-facing virtual assistants, ML for reviewing interactions and decision-making, and more.

Sentiment Analysis

In AI, sentiment analysis is like giving algorithms the capabilities to “read the room.” It looks at interactions, whether that’s reviews, social media posts, or emails, and determines whether a sentiment is positive, negative, or neutral. Sentiment analysis is a tremendously helpful AI tool and can help in making key shifts based on customer satisfaction.

LLM (Large Language Model)

Is an advanced machine learning model trained on vast amounts of text data to understand and generate human-like text. This type of model is designed to process and produce natural language by leveraging deep learning techniques, particularly transformers—a type of neural network architecture.

Insurance Chatbots vs Insurance Virtual Assistants

Two AI terms used frequently in the insurance industry are insurance chatbot and insurance virtual assistant. Insurance chatbots are growing in popularity and used to man websites to handle basic queries, but they lack the advanced capabilities of insurance virtual assistants.

Olivo, from Koios, is an insurance virtual assistant that goes above and beyond the traditional insurance chatbot. While an insurance chatbot can be a first step into the world of insurance automation, it pales in comparison to the capabilities of an insurance virtual assistant.

Here are some of the key differences between the two: