risk profiling

Mobility Data for More Accurate Risk Profiling in Insurance Companies

Línea Directa, a renowned leader in direct insurance sales domiciled in Spain, recently launched ‘Vivaz Safe & Go’, a pay-per-use insurance for users of Personal Mobility Vehicles. This revolutionary product covers the person and their mobility and can be engaged at the customer’s request. Instances such as this one depict the impact of mobility on the insurance world

Modern technology has not spared insurance companies, which have to meet higher customer expectations through interactive solutions and channels. Mobility data looks to be the next frontier for insurers who seek to maintain a competitive edge. 

Inaccuracy of Risk Profiling

Insurance companies can collect loads of statistical data to approximate a driver’s likelihood to develop a car loss. Every car insurer employs risk profiling, but the consumer has control in that he or she can continuously improve the profile. 

The risk profile depends on issues ranging from historical events to environmental characteristics. Risk models and policy prices are not rigid; they are bound to change with time. Unfortunately, some companies stick to the same critical elements of risk profiling for a long time. The result of this is a risk of lower business because the competition is upping their game. 

In the absence of superior technology, auto rate determination was as simple as ABC. The insurer based the assessment on the driver’s age, car type, location, driving record. If your risk profiling is still something close to this, your accuracy level is probably low. 

As the insurance market becomes more saturated and competitive, one of the best ways to remain relevant is to enhance the accuracy and precision of pricing and risk models as much as possible. Modern risk profiling is turning focus to data technology to identify a prospect’s risk factors. 

Custom-Made Insurance 

New business models have placed the driver’s profile as a requirement. Roughly 70% of insurance providers are combining existing data with that from external sources to create accurate profiles of their clients

Machine Learning is also an increasingly popular prediction generator, the result of which is complicated actuarial models. Insurance companies are also focusing on preserving efficient touchpoints with their customers as a risk management measure.   

Because of these dynamics, the following insurance business models now exist:

  • Pay-How-You-Drive (PHYD)
  • Pay-As-You-Drive (PAYD)
  • Try-Before-You-Buy (TBYB)
  • Manage-How-You-Drive (MHYD)

Each of these products has its segment in the market. For instance, younger drivers tend to go for the TBYB policy. More experienced or older drivers appear to be excited about PHYD and PAYD insurance policies. 

What are the remaining challenges?

Despite the strides made in mobility standards and compatibility of insurance, gaps and concerns exist at least in the interval. Leading among the concerns are issues about data processing and driver consent. 

The technical and economic challenges in the case of communication for connected cars cannot be ignored. Cellular bandwidth and electricity costs that go into this are substantial- the connected car is estimated to produce up to 25 GB per hour, the equivalent of several movies. 

Mobility Data for Less Risk

Alongside connected cars, modern bikes, scooters, and motorcycles can help deliver information regarding the driver’s behavior and usage of the item. AI-based apps are helping insurers with automatic alerts about a mishap. These apps can send images of the scene immediately. 

This form of application of mobility data not only simplifies the process for the consumer but also reduces costs for the insurer. Even a saving of US$10 can be significant, considering that auto insurance companies handle volumes of claims. 

Our focus is accurate mobility data. We are dedicated to providing facts about the surroundings so that customers can arrive at informed decisions. With regular updates, our mobility data is a single source of truth that insurance companies can depend on. Updated data is the secret for accurate risk models that ensure more accurate risk profiling. 

We have another differentiator. We are transparent. We communicate to our customers even regarding the implications of data to the finest details possible. 

We know that insurers need reliable mobility data to make risk models that are precise and accurate. Poorly priced policies can either push customers away or expose the company to financial loss. But with our mobility data, insurance companies can accurately determine the impact of human mobility on risk and price their policies relevantly. 

Conclusion 

The best insurers base their risk profiling on reliable data because they know this gives them a rich blend of accuracy and speed. For an insurance company that has never used mobility data before, getting started may appear tasking. 

Consider getting professional services from a company that has delivered for many others in the industry and beyond. Are you interested in more information regarding accurate risk profiling in insurance companies using mobile data before you can commit? Call or send us an email and we will get back to you.

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