Insurance carriers that are in the process of adopting the latest technology are able to reduce fraud, automate processes, and cut down on operational costs. As a result, insurers can focus on other mission-critical tasks such as attracting new policyholders and retaining existing ones. Innovations in technology benefit both insurers and their policyholders— whether through encouraging healthier lifestyles or providing a seamless onboarding experience.
Telematics Improve Pricing
Insurers use telematic devices that are installed in vehicles to track the driving behavior of their policyholders. They collect data relating to the speed, distance traveled, brake intensity, and accidents in order to set policy rates and enhance their insurance underwriting processes.
What’s more, telematic devices allow auto insurance carriers to use their customers’ driving habits as a way of giving them advice on how to become better drivers and lower their rates.
Traditionally, the insurance company would create a quote for a customer by asking several questions and use the responses as a foundation on which to determine the price of a policy.
Naturally, the insurance carrier would also factor in the fact that potential policyholders would use exaggerated claims or downplay certain aspects of their driving history.
Machine Learning (ML)
Machine learning solutions are receiving a lot of attention from many investors and companies and are one of the most interesting examples of insurance technology. Machine learning is an important branch of artificial intelligence (AI), which uses sophisticated algorithms to process and examine large amounts of data sets without human intervention.
Over time, the AI software is able to come to conclusions and make decisions by itself, which is one of the reasons why businesses are investing heavily in this technology.
Machine learning programs can automate critically important tasks such as calculating premiums, underwriting risk, managing claims, and preventing fraud,
Major Problem with Machine Learning Software Solutions
The hurdle to machine learning’s massive adoption in the business world is the fact that it needs huge volumes of data in order to properly train the algorithm (neural network).
Complex algorithms need datasets in order to discover hidden links within the data, which enables them to make logical inferences and predictions.
Therefore, machine learning software tools are only as good as the data that’s used to train them.
Lack of Transparency and Cryptic Decisions
Machine learning-based software tools are vulnerable to the black box problem. Basically, after a while, the decision-making process of machine learning algorithms becomes impossible for humans to understand, which is why insurance providers are struggling to implement them.
The Importance of Rules Engine for the Insurance Industry
Modern carriers need to make changes to their current insurance plans and get to market with new products immediately and without any mistakes. Traditionally, these tasks used to require an experienced software developer. Nowadays, every innovative insurer relies on a rules engine to develop and manage digital insurance products.
With rules engines, insurers can drastically lower their time to market by enabling non-technical subject matter experts to create new insurance plans and make comprehensive changes to existing ones in a few minutes. Using conditional “if-then” statements called business rules, carriers can create sophisticated underwriting, anti-fraud, and claims management processes.
