Disruptive insurtechs and other digital-first brands are experimenting with new business models and discovering additional revenue streams. Insurance carriers who are the first to adopt new technology are able to innovate, reduce costs, cut down on fraud, and automate services, which enables them to concentrate on other mission-critical tasks such as customer acquisition.
Internet of Things (IoT)
The Internet of Things (IoT) describes the entire network of physical devices with built-in software that automates the exchange of information with other devices using the internet.
IoT allows providers to leverage data from connected devices to better understand their clients’ behavior and needs.
As a result, insurance carriers can use the real-time data generated from various IoT devices to enhance risk management and assessment and determine rates.
Telematic devices are installed in vehicles that track the drivers’ behavior. The driving data is then shared with carriers. These devices collect data such as speed, braking, distance traveled, location, and accidents which are used to set policy rates, enhance insurance underwriting, and offer discounts.
Telematics enables auto insurance provers to better understand the driving habits of their customers in order to make recommendations on how to be better drivers.
For instance, the carrier could suggest that the policyholder steers around corners more gently.
Therefore, carriers can set better prices since they have access to reliable data and don’t have to rely on asking customers.
One of the most popular insurance industry trends is machine learning, which is an exciting and important branch of artificial intelligence (AI). Machine learning programs are powerful AI-based software that relies on complicated algorithms to process and study data sets, all without the need for human supervision. After some time, the machine learning algorithms can make decisions by themselves, which is one of the main reasons why the technology is exciting.
ML apps automate important operations such as premium calculation, underwriting, insurance claims processing, and fraud detection.
The Problem with Machine Learning For Insurance Companies
Complicated machine learning algorithms study datasets in order to uncover hidden relationships within the data. This is how they are able to make inferences and make predictions.
However, the problem is that machine learning software requires extremely large volumes of high-quality data to make accurate decisions. So, organizations looking to deploy machine learning need to make sure that they possess huge quantities of data that are properly structured and relevant.
The Importance of Rules Engine for the Industry
Carriers need to make constant changes to current policies and reduce time to market without any errors and hiccups. Traditionally, this required a team of experienced programmers. However, business rules engines have transformed the insurance industry.
A business rules engine empowers insurance companies to :
- Drastically reduce time to market
- Seamlessly develop Insurance products with instant input from all relevant parties across the organization
- Empower subject matter experts to assume full control of their products
How Do Rules Engines Work?
A business rules engine is a flexible software component that relies on conditional “if-then” statements called business rules. Business rules are a proven way of optimizing critical tasks of companies.
Laws, regulations, and best practices can be represented by business rules.
For that reason, insurers use rules engines to automate time-consuming and complicated tasks, and boost collaboration, while reducing potentially expensive errors.