Today’s auto insurance claims journeys are fragmented, complex, and manual. Processing claims requires significant inputs from customers, insurers, repair-shop networks, and rental providers, and it often relies on enriched datasets coming from disparate sources.
These mobility-specific datasets play an essential role in ensuring the efficiency of various risk models to customize auto insurance apps.
But how do auto insurance companies leverage the capabilities of mobility data to build robust auto insurance claim operations? What are the challenges with real-time data they face, and how to overcome these obstacles?
In this blog post, we’ll explore the scopes of real-time mobility data in innovating the existing auto insurance market. But before that, let’s deep dive and understand how IoT (Internet of Things) is revolutionizing the auto insurance market.
How is IoT Revolutionizing the Auto Insurance Industry?
The Internet of Things (IoT) is rapidly transforming and providing a plethora of opportunities to the auto insurance industry. In the existing connected mobility ecosystem, auto insurance companies can install IoT/telematics devices to vehicles and enable a robust IoT platform to manage the data coming from vehicles’ ECUs.
Furthermore, they can utilize this data to leverage the capabilities of predictive analytics and remote monitoring technologies to get real-time insights into the vehicles’ health and performance. Utilizing these insights, they can offer premium services to customers. These services can include real-time alerts for vehicle maintenance, emergency assistance, stolen vehicle assistance, and a lot of data-driven insurance solutions.
Consequently, it helps auto insurance providers to enhance the efficiency of the insurance claim journey. Let’s scroll down and understand it in detail.
Catalyzing the Auto Insurance Claim Journey with Data-Driven Approach
Enhanced Automated Claim Process Experience
Policyholders have to consider several data checkpoints while considering genuine auto insurance process including the filters to identify false claims. Overall, it’s a time-taking process that requires passing through a plethora of data touchpoints between the policyholder s and an auto insurance company’s employee.
It’s one of the biggest hurdles towards a frictionless customer experience that still requires to be addressed by several auto insurance companies. But with the advent of technology, auto insurers are rapidly adopting a data-driven approach to automate this process, improving their fraudulent claim management system. Possibly, they can do it by comparing the collected data from the vehicles’ TCU (Telematics Control Unit) with the customer’s provided data while claiming the insurance. Furthermore, they can leverage the capabilities of ML (Machine Learning) algorithms to automatically validate the claim process. For instance, if the provided location, speed and video data matches the vehicle’s speed, location, road and transport compliance & regulations at the time of incident, the claim application is automatically processed further. This procedure can help auto insurers to achieve the following two goals:
Hassle-free creation of loss, claim and incident reporting, enhancing customer experiences
Elimination of fraudulent claims via deeper insights into accident reconstruction analytics
Challenge
Collection of rich datasets from a large fleet of vehicles in real-time, avoiding data loss that is essential for ensuring the credibility of the auto insurance claim is a time-taking and expensive process. And if by chance some essential packets of these datasets are missing due to unprecedented data loss, it can diminish the authenticity of the insurance claim.
Initiative of Usage-based Insurance (UBI) Model
Another technological evolution in the auto insurance industry is the adoption of Usage-based Insurance (UBI) model for calculating premiums. Presently, auto insurers can provide personalized premiums to the policyholders, depending on individual driving patterns. It’s possible by harnessing the power of data analytics that relies on connected vehicles equipped with telematics devices for data collection.
For instance, the auto insurers can analyze vehicle’s telemetry data related to speed, acceleration, braking, and more to calculate premiums. Consequently, it will play an essential role in reducing road accidents, further ensuring safer driving experience. Some of the use cases include:
Driver Behaviour Monitoring
Mileage Verification
Challenge/Concern
As it’s clear how vital data is when it comes to ensuring the efficiency of a UBI model, maintaining its integrity is still a challenge. Hackers and malicious actors can effortlessly gain unauthorized access to this data, further exploiting the vulnerabilities in the cloud architecture.
All this is due to the challenges of creating robust data authentication methods to reduce attack surfaces. Data teams still have to re-engineer complex codes to ensure the efficiency of their auto insurance claim application. In a nutshell, it’s an expensive and time-consuming process.
An Overview of Logic Behind Dynamic Insurance Premium Pricing
The success of the insurance company is purely dependent on the competitive premium pricing and the cover to the subscribers. As the advancement of the technology of shared mobility is trending in the urban cities, there is demand for insurance premium based on usage. If insurance gives a premium with lowest cost, then it is not profitable to business or may not be able to sustain in the market. So, Insurance companies are looking towards fair premium pricing rather than lowest premium. Then, questions arise.
How can this fair insurance premium based on usage be achieved? Yes, this is possible by the telematic integration to the vehicle and ML algorithm to predict the risk. The following are the concepts of fair premium pricing based on the real time risk.
Why Risk varies? If a vehicle is parked, the risk of accident is low and when on highway, risk of accident is more.
So currently the insurance companies are working with average estimation. If they place a premium on the higher side of the risk, then it will be beneficial to the insurance company but overcharge the subscribers. If the company places premium lower side, it will be loss for insurance companies or required to shut down.
The ideal situation for insurance companies would be premium pricing changes with risk with some buffer built in operation and profitability. Because closer the premium to the risk then it will be a win-win situation for both insurance companies as well as to the subscribers.
This can be achieved by the Telematic device plugged-in in the vehicle and connected mobility platform will enable the vehicle real-time CAN data and GPS details transfer to the cloud.
Based on the data availability and real-time tracking of the location of vehicles enable the insurance companies to assess the risk and charge the dynamic insurance premium.
Let the scenario of the vehicle moving from village to city, the risk of the accident will be less in village and in city it is more. Likewise, the premium pricing will increase as the vehicle moves towards the city. It will be profitable to the company as well as to the subscribers.
So as explained above the cost of the premium is directly proportional to the risk. So, to decide the real time premium pricing, the risk depends on the Driver, Car value, Speed and Location.
This data can be available with connected mobility which will enable the process to be autonomous. As fleet owners can give the user remote key to enable ingestion on and off, by which the insurance company get to know who is driving or logged in, Value of the vehicle will be known factor, Telematic device in the vehicle enable the vehicle CAN data transfer to cloud where insurance companies can fetch the details like GPS location and speed at which vehicle running. Combining all these data (Driver info+ Vehicle Value + Speed + Location) and construct the risk contour with the help of machine learning algorithm.
So, the insurance company predicts the near real-time risk assessment and dynamic premium pricing will give the competitive advantage.
Intelligent Risk Predictions Based on Driving Behaviors
Vehicle insurance companies can use predictive analytics and ADAS technologies to alert fleet managers about risky driving behaviors, such as over-speeding, unbuckled seatbelts, etc. With the capabilities of predictive analytics, they can analyze historical data related to road safety compliances, accident records, and more to identify risky driving patterns.
For instance, they can predict the risk of an accident if the vehicle’s real-time data related to speed and location don’t match the road safety compliances of that location. Furthermore, they can use these insights to send live notifications to the fleet managers about it. After receiving these live alerts, fleet managers can notify the driver to control the speed in ADAS-equipped vehicles.
Even if an accident occurs, they can find its root cause by deriving valuable insights from the ADAS data to deeply analyze the severity of the event. As a result, it will help auto insurers to accurately access the damages, thereby streamlining the overall auto insurance claim process.
Challenges
In the existing connected mobility ecosystem, auto insurance providers work with average estimation. For instance, if they place premium on the higher side of the risk, the outcomes are profitable for insurance companies, but loss-making for the subscribers. On the contrary, if the insurance company places premium on the lower side, it’s a loss-making deal for them.
Ideally, the auto insurers require a dynamic insurance calculator that changes with the increasing risk, whenever the vehicle crosses certain threshold conditions of risk analytics in real time.
However, it completely depends on the real-time streaming from various telematics devices that requires to be securely ingested, streamlined, and transformed into valuable business insights. It’s still a hectic process for various auto insurers where they need to overcome challenges of data latency.
Driving the Future of Auto Insurance Companies with Zeliot's Condense & Condense Edge
Zeliot’s Condense, a verticalized cloud platform, and Condense Edge, a low-memory footprint embedded firmware, can revolutionize how auto insurance is calculated. By leveraging the capabilities of Condense Edge, auto insurance companies can digital twin their fleet of insured vehicles for remote diagnostics to get real-time insights into vehicle’s performance.
They can configure rules on the telematics device to detect and securely send threshold data from the vehicles’ TCU to Condense. Here, Zeliot’s Condense automatically ingests this data, streamlines it and transforms it.
On top of it, auto insurance companies can set Custom Business Logic to configure conditions for identifying anomalies and deviations in the vehicles’ expected behaviour. So, whenever there is an anomaly in telemetry data according to the predefined rules and logic, auto insurers can utilize these insights to send live notifications to fleet managers.
It will help both auto insurers and their customers i.e., fleet operators or owners to achieve an ideal scenario for insurance calculation and risk identification.
Summing it up,
Internet of the Things (IoT) and real-time telemetry data are rapidly revolutionizing the auto insurance industry and for good reasons. It’s going to be a win-win situation for both auto insurance companies and customers by adopting dynamic insurance calculation methods which changes with the increasing risk in real-time. Zeliot’s Condense and Condense Edge is making it a possible journey and after reading this blog post you already know why.