Machine learning algorithms are bringing a lot of insights with respect to insurance claims and customers behavior. By usage of latest tools & technologies for data gathering, such as telematics and mobile driving apps, it is possible to tackle the challenges associated with customer acquisition, retention as well as reducing claims costs.
Currently, auto insurance industry faces challenges related to the following aspects of customer acquisition:
- Right quote and coverage provided for a customer
- Competitive quotes and rates for attracting new customers
- Customer retention initiatives such as better quotes and discounts for existing customers
Believe it or not, car insurance rates aren’t arbitrary numbers made up by auto insurance providers; they are the result of careful calculations. Using customers’ information and claims data, insurance companies use sophisticated algorithms to determine the cost of an insured customer to the company. However, the current rating algorithms are generalized, and there is a gap in providing personalized rating specific to the customer.
Telematics devices and mobile driving apps can play a more significant role in avoiding unnecessary claims by providing necessary metrics of the driver and the vehicle, which in turn saves cost and resources for both the customers and the insurance company. Once insurance companies understand their customers better, they can provide quotes and discounts to retain existing customers and attract new customers.
Personalized ratings can be built by using a hybrid approach of combining existing data metrics from claims and customer information with customer driving behavior. These statistics can be captured from telematics data using dongle devices and mobile driving apps, the next generation tools for identifying risky and safe drivers.
To get to the personalized quote level, we need effective rating algorithms. The data generated can provide insurance companies with meaningful insights to help create new models that can be trained to derive a score for identifying risky drivers.
Here’s how the parameters mentioned above can be used to determine the scoring model:
After the models are trained with customer personal data and driving history, they can then be trained with continuous data from telematics devices and mobile driving apps to calculate driving scores based on usage, behavior, and geospatial data.
Understanding the customer is a continuous process, and it uses the following parameters:
- Data Gathering and Transmission: The sensors installed onboard the vehicles and smartphone collector applications record driving behavior and transmit the data.
- Data Collection and Aggregation: Insurers and device providers collect the data and make it available for the models to derive metrics.
- Data Analysis: Insurers’ AI models review and analyze the collected and aggregated data giving them the ability to customize policies based on driving behavior.
- Driver Evaluation: Drivers participating in telematics programs access the data online and get an opportunity to track and positively change their driving behavior, which can lead to reduced premiums and claims.
Insurance companies can create new or enhance their existing models to derive better insights for rating algorithms to offer an improved quoting process. In fact, the insurance companies can encourage their customers to enhance their driving scores by tracking their driving behavior. Here are some aspects where the insurers can work with their customers:
- Encourage customers not to use their phones while driving which can cause fatal distractions
- Alert them in case of speeding or vehicle-related issues like low tire pressure or engine alert
- Offer vehicle location in case of theft