How Machine Learning is Disrupting Insurance
The insurance sector has always depended on data when it comes to calculating risks and coming up with more personalized ratings. However, these days, the industry is going through a deep digital transformation because of technologies like machine learning. Insurance carriers can increase their efficiency and productivity, detect fraudulent activities, and even improve their customer service efforts. With that said, here are a few ways machine learning is disrupting the insurance industry.
Also, Read: How Healthcare Industry Leverages Machine Learning Through Wearables
Data Insurance—Sophisticated Algorithms for Rating
One of the foundations of the insurance industry is rating. In the world of insurance, there’s a famous line that goes, and there’s no such thing as a bad risk, just lousy pricing. This refers to how companies can accommodate risks so long as they find an ideal pricing match. However, many still depend on conventional methods when they evaluate risk. One example is property risk, where companies may utilize historical data for specific zip codes. Customers are usually assessed with outdated indicators like loss history or credit score.
With machine learning, insurance providers have new methods and tools when classifying risks or predicting more accurate pricing models for reducing loss ratios. Vehicle telematics that facilitates information flow to aid in usage based insurance, for example, uses machine learning technology.
Process and Automation Improvement
Like any other industry, insurance companies are regulated by specific legal requirements, processing claims and responding to queries by thousands. However, through machine learning, carriers can improve the claiming process by automatically moving them throughout the system, from initial reporting to the claim’s analysis and customer contact. In specific cases, it may not even require human employees’ work, enabling them to focus their efforts on claims that are more demanding.
Better Consumer Lifeline Value Prediction
CLV or consumer lifeline value refers to a complex metric representing a customer’s value to a business using the difference between the expenses incurred and revenue gained. All of which are projected into the relationship with the customer. Most insurers predict the CLV through data pertaining to customer behavior that enables them to evaluate their profit potential and create personalized marketing and promotional offers. These machine learning behavior-based models can also be applied to cross-buying or forecasting retention—critical elements in the future.
Beyond that, machine learning can also aid insurance carriers in anticipating specific customer behavior, such as the maintenance or surrender of policies.
A common concern that many insurance companies share is fraud, and for a good reason: it costs the industry billions every year. However, thanks to machine learning, it’s easier for carriers to prevent fraud from occurring. After all, the technology can help identify potentially fraudulent claims and take action on them accordingly.
There aren’t many technologies that are on their way to causing significant disruption in the insurance industry machine learning. However, because data plays a critical role in how insurance providers work, it makes sense that carriers ride the wave of digital transformation and begin implementing ML-based solutions for uncovering insights. The reliance on this technology will only get more robust, thanks to all of its benefits.