HOW IS GEN Z SHAPING THE FUTURE OF CAR INSURANCE?

By Evan Davies, CTO, Solera

 

Generation Z is reaching its mid-20s in 2021. This generation has grown up in an age when companies like Amazon, Uber, and Airbnb use AI to simplify purchasing, communicate directly with customers, and apply analytics to their business. Gen Z probably doesn’t remember services or products without voice activations, personalized buying suggestions, and chat bots offering instant interaction. As they become more influential as consumers, their expectations for services and interactions will include many AI-based offerings. This, combined with the purchasing power of Millennials will require all businesses to evolve if they want to meet these collective expectations for technology and rise to the new standard of on-demand interaction.

To come to terms with this, the insurance industry is quickly adapting and integrating AI into business operations and claims infrastructure. In a recent report, McKinsey projected that there is a potential annual value of up to $1.1 trillion in additional business revenue if AI is fully embraced in the insurance industry alone. The sector itself has been slow to mobilize digital transformation in the past, but the disruption seen in the Covid-19 era has pushed the adoption of AI and other next generation technologies up the priority list for many insurers. That said, it’s not as simple as adding something like visual intelligence to the photo estimation process.

Computer vision and the technology that enables it must be underpinned by two fundamental elements: quality data and knowledge of vehicle repair processes. AI-based solutions must be trained and supported by the combination of these two components to ensure consistency of results at every stage. Only then can insurers advance their algorithms to maximize accuracy, ensure faster claims resolution and enhance the customer experience.

 

Evan Davies

Image Recognition and Processing Using AI

For those who have recently filed an insurance claim of any kind, you are probably familiar with the image capture and upload process. While image capture is not necessarily new, the technology at work evaluating and producing the outcomes is quite new and can be complex.

Automation tools improve the first notice of loss (FNOL) and triage processes by speeding up review of damaged photos, identification of total loss vehicles and supports identification of the next best action for repairable vehicles. This means that decisions that affect claims outcomes for the insurer, repairer and insured are made at the beginning of the claims process to streamline the workflow and speed up the claims and repair process for all involved.

When it comes to AI photo based estimating, there is no ‘one-size-fits-all’ solution with more than one way to approach the process. One approach is to train AI models by learning from historical claims data and related damage photos. Another approach is to use a training data set containing annotated or “labeled” photos.

 

Get Quicker and More Reliable Repairs

In addition to customer experience, AI is enabling insurers to maximize the skills and outputs across their own workforce. In the absence of an AI-based estimating system, trained appraisers are required to produce repair estimates. However, with the use of an AI-system, those skilled workers can be reassigned to more complex cases.

Providing an accurate estimate of the size, position, and severity of the damage plays a crucial role in getting the repair cost correct. There are many variables that influence the cost – from material and geometry to accessibility and thickness. Modern vehicles are made of alternative materials, such as different types of steel, aluminum, and carbon fiber, which inevitably impacts cost. Many new vehicles are also fitted with sophisticated advanced driver-assistance systems (ADAS) which need to be considered when assessing damage. This is a lot to consider and, hence, why the repair science element of any AI-based estimating is so important for safe repairs and correct claims valuations.

 

Blending Data and Repair Science

As with any automated technology, the algorithms that power insurance solutions must be trained and supported by the right set of data to ensure consistency of results at every stage and for every touch point served by AI. It’s not just about bringing AI to an existing workflow. Estimating using AI is about blending repair science technology with a database of historical claims and images gathered over years of servicing and supporting the industry that makes this evolution so powerful.

The combined approach of these two types of data enhances machine learning algorithms to drive efficiency and accuracy. It provides an holistics and integrated approach that can effectively increase accuracy and performance across an entire claims workflow, compared to simple AI image recognition point solutions.

 

Future Proofing for Digital Native Generations

Gen Z are pushing the boundaries of digital communication once again – but we know it won’t stop there. Now, more than ever, with the increasing use of digital channels by all generations and younger generations’ expectation for it, AI is becoming trusted by service providers around the world, including those in the insurance industry. Implementation of next generation solutions like the AI now found in many repair estimating solutions is accelerating and must continue doing so if insurers are to navigate ever-changing consumer needs.

It’s widely acknowledged that this is the future of insurance and beyond. Yet, with any new technology adoption, it doesn’t come easy. It takes partners with the right background and expertise to help chart the path, expand adoption and rewrite the standard of service for years to come.

 

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