During the last few months, words like AI, ChatGPT, and Bots have become recurrent when talking about the future of IT.

A famous magazine mentions AI as the very first option among The Top 10 Tech Trends in 2023:

“In 2023, artificial intelligence will become real in organizations. AI… will enable any business to leverage its power to create more intelligent products and services. “

(forbes.com in The Top 10 Tech Trends in 2023)

For Us, HB Solutions Enterprises, an IT company operating in the InsurTech field, it’s important to understand how to make #AI a reality in the insurance industry and leverage its power to bring vast improvements to the business.

For this purpose, let’s go through some points that will explain how the use of artificial intelligence can be game-changing in the insurance context.

What is AI?

Artificial Intelligence (AI) is a science field concerned with building systems capable of recreating or exceeding human-like capabilities such as reasoning, learning, environment perception, decision-making, and problem-solving. Some of its subsets are:

Machine Learning (ML)
Expert Systems
Natural Language Processing (NLP)
Speech Recognition
Vision While these features can be applied to a wide variety of contexts, in the Insurance business, AI’s application is often centered on subset 1. Machine Learning (ML) which “focuses on the use of data and algorithms to imitate the way that humans learn”. (IBM in What is machine learning?)
Machine Learning Application

Machine learning has many applications in the insurance industry, however, the most common is to help insurers in better-predicting risks and preventing losses. Here’s how a machine learning model works in a nutshell:

The process begins with the collection and preparation of accurate and relevant data. In the insurance domain, this data may include information about policyholders, such as age, occupation, and other relevant factors.
Once the data has been collected and prepared, a suitable machine learning algorithm is selected and trained using the dataset.
The trained machine learning model is then applied to new data, allowing it to make predictions based on the patterns it has learned during the training phase.
An efficiently trained ML model can be used to predict important factors in the insurance business. These may include detecting fraudulent claims, identifying policyholders who are most likely to file a claim based on their history, and improving the customer experience by providing accurate premium pricing and personalized policies.

In conclusion, as more and more insurers adopt this technology, we can expect to see significant improvements in efficiency, profitability, and customer satisfaction. By harnessing the power of machine learning, insurance companies can stay ahead of the competition and provide better service to their policyholders.

by Cristian Hoza.