How Insurance Companies Are Incorporating Machine Learning

Big data is named that for a very good reason. It refers to quantities of information that are so large or complex that it’s impossible to process using traditional methods. We as a species have collected more data within the past two years than mankind has in its entire history, yet we are unable to process it sufficiently. The variety of industries that are offering up data are constantly growing, including electrical engineering, telecommunications, vehicular technology, social media, voice analytics, connected sensors and wearable devices.

One of the great things about machine learning is its ability to handle big data like this. Machine learning takes a large amount of information and detects meaningful patterns. From there, it can make predictions at a rate of speed and accuracy that far surpasses humans. Machine learning and its ability to adapt is being used in almost every industry nowadays and the world of insurance is no exception.

Auto insurance is all about data when it comes to claims and premiums. When a driver seeks to purchase auto insurance, there are a number of factors that are considered, and the higher a person’s risk factor, the more it translates into real-world decisions like the costs of premiums.

Most insurance companies are only able to process 10 to 15 percent of the data they receive. This data is stagnant and stored in databases. This structured data is not used to its full potential, and it doesn’t display any predictive modeling behavior. Machine learning provides the boost they need to face their competition and enhance customer satisfaction. AI is also being explored in other departments as well, such as underwriting, fraud detection, and loss prevention.

Ultimately, direct machine-to-human interaction will begin from the moment the first interaction occurs between the customer and the insurance company. On the front-end, 24/7 chatbots have made it easier for potential customers to have answers at the ready, as well as existing customers filing claims. All while freeing up in-house employees to focus on other tasks. On the back-end, clients receive personalised products that are created by machine learning algorithms that have tailor-made packages based on users’ profiles.

The ultimate goal is to combine the Internet of Things (IoT) to share omnipresent data in an open source platform. This would apply to everything from smart homes to smart cars to the healthcare industry. 20 percent of companies have departments solely dedicated to guide and monitor the progress of machine learning.