In this Opinion piece, Philip Mott, Senior Director, HERE Technologies, takes a look at the value of location data; risk, quote and claims.
Insurance companies have always relied on location data to assess risks and manage claims. What is changing, however, is the precision and the freshness of that data. Insurance providers can now draw on more precise and fresh data to the second or the centimetre, unleashing new opportunities when brought together with other sets of data to deliver a digital representation of reality.
Not only is this digital transformation key to better risk assessment, assistance and claims management, it is also the way to increase customer loyalty. By gathering and making sense of data coming from multiple sources, insurers can anticipate damages and proactively recommend appropriate actions to mitigate risks.
In the context of COVID where customers are becoming more demanding about the value they get out of their insurance, a location platform that brings together information from multiple sources in real time enables insurers to take their capabilities to the next level.
Determining the best rate based on accurate risk profiles
The first task of any insurance company is to assess the customer’s risk profile, or the risk posed to insure an asset. Accurate risk profiling can, in turn, lower insurers’ rates by reinsurers and increase customer loyalty as their premiums sink.
When insuring a property, location is a key factor influencing risk. Combined with other factors such as age, construction type, property type and claims history in the vicinity, location data can help assess the risk associated to any property. High-precision mapping, underpinning sophisticated geocoding services, is needed to accurately pinpoint the location of a property in an urban or rural environment, sometimes on complex and evolving terrain. This data helps evaluate risks that could destroy or damage property, including perils from air (wind, hail, tornado, lightning), water (river flood, surface water, coastal storm surge), earth (earthquake, brownfield) and fire. This is the approach that Hazardhub is taking along with many other insurers and reinsurers.
The automatic integration of this location data into underwriting is now made possible by emerging technologies such as robotic process automation (RPA), artificial intelligence (AI) and machine learning (ML), and advanced analytics, increasing accuracy while decreasing manual labour. It turns the art of underwriting into a predictable science, embedding risk insights directly into pricing logic.
The quality of the data is key to this process. This is where data cleansing solutions for addressing and geocoding – such as the ones provided by general agent Agile Risk Partners – come in. Once the data has been cleaned, it can be enriched with risk layers to provide insurers with more context. A geocoding engine, for example, turns a list of addresses into a map and adds on top information about risks related to crime, flood or subsidence. In this way, underwriting decisions and pricing become more accurate and the performance of the portfolio is improved.
Car insurers also use location data to evaluate their drivers’ behaviour by profiling how, where and when they drive. In order to adapt to customers’ risk profiles, car insurers rely on technologies that leverage speed limits to analyse how fast drivers are going against the actual speed limit on the road and assign them a risk score based on that comparison.
In the same way, several companies generate precise trip data from in-vehicle connected devices to offer behavioural pricing. More recently, data coming from vehicles’ sensors such as windshield wipers, tyres, brakes and warning lights have been used to add context to any given road situation. As no car brand can claim global coverage, it is crucial for the original equipment manufacturers (OEMs) to share sensors data to reinforce safety on the road for every user.
Accelerating claim management
In claims, time is money, so speedy resolution equals huge cost benefits. In the property sector, claims that are not settled within 30 days are estimated to be 50% more likely to end up being disputed, with legal action adding to costs. In the same way, auto claims cost US and European insurers approximately $360 billion annually. The smart application of telematics and sensors data could substantially reduce this figure. Instead of filing an extensive report, insurers can directly seize vehicle sensors and traffic data as well as video streams from dashcams. Road conditions, traffic, weather conditions, maximum speed, number of lanes and places (such as crossings and dangerous spots) help apprehend the car’s environment and handling to better evaluate what has happened.
Location data can also be used to flag suspicious patterns, such as the same kind of accidents happening at the same place. One of the biggest European reinsurers is also known to have used traffic data to check the veracity of flood-related claims. If the area were indeed flooded, the traffic should be lower than usual. If not, it indicates potential fraud.
Not only does location data have application in claims management, but also in assistance services, helping those in need to receive rapid support from breakdown and emergency services.
Embedding location data in the claims and assistance processes to capture the precise location of each event enables additional contextual data to be captured. The data intelligence layer can thus be built around profiling and processing of existing claims which helps in predicting and modelling future claims. Now, with real data streams, insurers can be warned about an accident having happened before they even receive a claim and proactively reach out to their customers with the help they need, opening new lines of business opportunities.
Improving clients’ safety in real time
Accelerating claim management is one thing. Preventing accidents is another ball game altogether. And one that location data, once again, can support. Some companies provide customers with a telematics device that connects to a vehicle’s onboard diagnostics port (OBD-II). The device allows the company to notify customers via a mobile app about the health of the vehicle and recommends maintenance that could prevent safety issues.
For cars that don’t have advanced driver-assistance systems (ADAS) functionalities, smartphone cameras can now be used to gain a Forward Collision Warning system. Additionally, these can monitor the driver to detect driver distraction. New companies like dreyev build upon this by using AI and ML to detect potential dangers on the road ahead, while at the same time detecting distraction, drowsiness and driver attention, using both front and rear facing cameras of a mobile phone to prevent crashes. Some apps go one step further and use gamification to encourage a better driving style based on the real-time data they receive.
When this data is linked to thousands of profiles, it can result in revisions to insurance premiums and reflect the real risk being assumed. Insurers could consider taking into account other criteria such as the addition of autonomous driving features. Location can provide the tools to improve safety and provide visibility of risk, claims and premiums.
Ingesting map and sensor data from many different sources is key to accurately assessing risks, accelerating claims management and saving lives. The richness of today’s location data, both static and dynamic, combined with 3D mapping technology makes it possible to represent the world digitally down to the centimetre. Combining these assets with third-party data, ML and predictive analytics create a transformational opportunity for the insurance industry to offer value added services, adapted to the needs of every risk profile. The introduction of fleets of autonomous vehicles requires user-based pricing and easy claim management. With these trends coming together, the need for a location data platform that can render a digital representation of reality has never been more obvious.