Insurance - Transforming car damage assessment in auto-insurance claim process
Put in production a trustworthy, accurate model to detect defects
Build a relevant training dataset, with high quality labels, on which to train the model
Get various stakeholders to collaborate effectively to ensure the labels accuracy and quality
User-friendly image UI with precise labeling tools
Powerful API to easily and quickly import and export large volume of data
Collaboration features and quality review workflow
Advanced pre-annotation labeling capabilities by applying online learning
Third party labeling provider recommended by Kili with highly skilled labelers
A global insurance company was transforming the procedures of its auto-insurance claim process, especially in the vehicle damage assessment. The goal was to simplify and accelerate the inspection process with equivalent, or if not, better accuracy of repair cost estimation to improve productivity and customer satisfaction.
Artificial intelligence was adopted in the digital transformation process of damage assessment, Kili Technology contributed its role in enriching the model with high-quality datasets through data annotation.
In the old days, the company’s capability to assess damages to estimate the repair cost would involve manual inspection of plenty of not-always-high-resolution photos taken from mobile cameras, phone calls, and onsite physical inspection by assigned assessors. While this process is already time-consuming enough on normal working days where car accidents can happen on busy streets, in holiday seasons where road traffics (and unfortunately, car accidents as well) is peaking – car damage inspection for auto-insurance claim assessment process would take ages. The consequence of this manual inspection process was a scenario where customers had to do repeated calls to check up on progress status – only to hear instructions to call again, with no certainty. It was no surprise that as weeks progressed, the customers’ tone over the call increased with growing impatience.
More importantly, quick detection of different degrees of damaged car body parts would translate into better visibility and faster calculation of accurate estimation of repairing cost – depending on which part of the car component got wrecked and how bad the damage is.
To implement this solution, the company needed to ensure that the AI model is trained well to deliver an accurate detection of defects. The insurance company found several challenges to overcome when building the AI model for car damage assessment. First of all, building a proper dataset itself to train the AI model is a challenge in itself as currently there is no, if not very limited, publicly available dataset of broken or damaged cars.
Therefore, the company had to build its very own dataset that needed to be extensive in terms of different types of cars, types of body parts within the car, and also different types of damage along with varying degrees of severity.
It is very difficult to get relevant public datasets of damaged cars that are clearly labeled. Even if we found one, it is not extensive in terms of types of wreckage, or how bad the wreck is. Our AI model needed to be very accurate, then a specifically tailored labeled dataset had to be developed.
More importantly, aside from the quantity of the dataset, the company needed to ensure the quality of these training datasets being developed. Quality inaccuracy contributed a highly critical role as it would determine the repairing cost estimation, and miscalculation of this cost would either produce loss to the company or customer complaints. The company also found it challenging to find a way to increase speed and productivity in training the AI machine, as manual labeling and collaboration between assessors to label the data, data scientists, and the machine learning team would be time-consuming and expensive.
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Understanding the challenges it was facing, the company realized the importance of partnership with a company that could offer solutions in training data preparation, especially in terms of data annotation. When exploring numerous data annotation companies available in the market, the company discovered Kili and tried different features of the tool. The company was excited that the end-to-end solution on data annotation at Kili enabled the company to not only annotate the extensive amount of unstructured images of damaged car body parts easily, quickly, and at scale, but it also enabled the company to set up a fluid collaboration between the assessors, data scientists, the AI division team, and the external labelers sourced by Kili as the company needed a handful of experts to help annotate the bulk data.
It was an end-to-end solution for us: we got not only the very simple tool, but also a powerful API, high-quality experts as external labelers, and automated predictions in the process.
Moreover, the company favored the application of generating automated predictions of labels by applying online learning in the annotation process. This leveraged the company to speed up the labeling per each image of the damaged car, making the overall development of the training dataset of damaged car images for the AI model to be much faster than the company expected.
As a result, company productivity and customer satisfaction skyrocketed. Using an AI model to detect car damages also improved the accuracy of cost estimation, reducing the level of errors done in manual inspection, saving up 17% of the “unforeseen” repairing cost due to inspection errors.
I believe we reduced more than 32% of the number of what we describe as “follow-up calls” where the customers ask for the status update of their claims while damage assessment is ongoing. What once took ages, we made it real-time. We keep on getting positive feedback from customers.
Adopting artificial intelligence to inspect car damages in auto-insurance claim processes could significantly reduce efforts and time taken by assessors, speeding up the overall claim process
Accurate AI in car damage assessment in auto insurance could result in an impactful estimation of repairing costs in real-time. To achieve this, training the AI model with accurately labeled data is crucial.
The unique solution to build training datasets with data annotation specifically addressed to inspecting different types and severity of car damages will greatly improve efficiency and productivity as positive results
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