Transforming car damage assessment in auto-insurance claim process

Transforming car damage assessment in auto-insurance claim process

Key Impact

  • 17percent

    Reduction in repairing cost due inspection human error

  • 32percent

    Reduction in customer follow up update calls

  • Increase in customer satisfaction


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.

When AI speeds up assessment of car damage and repairment cost

In the old days, the company’s capability to assess damages to estimate the repairment cost would involve manual inspection of plenty of not-always-high-resolution photos taken from mobile camera, phone calls, and onsite physical inspection by assigned assessors. While this process is already time consuming enough in normal working days where car accidents can happen in 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.

To prevent turning this impatience into non-subscription, the company considered artificial intelligence as a digital transformation solution to this highly manual damage inspection process. Using a computer vision model, automated detection of damaged body parts can be done very quickly from the severe wrecks to the thin scratches, at scale. 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.

The challenges of building AI-powered car damage inspection model

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 car, 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 cleanly labelled. Even if we found one, it is not extensive in terms of types of wreckage, or of how bad the wreck is. Our AI model needed to be very accurate, then a specifically tailored labelled dataset had to be developed.

– Apolline, Director of Digital Transformation.

More importantly, aside from the quantity of the dataset, the company needed to ensure the quality of these training dataset being developed. Quality in accuracy 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 labelling and collaboration between assessors to label the data, data scientists, and machine learning team would be time consuming and expensive.

Why Kili Technology

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 labellers sourced by Kili as the company needed a handful of experts to help annotate the bulk data.

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 labelling per each image of damaged car, making the overall development of training dataset of damaged car images for the AI model to be much faster than the company expected.

It was really 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 labellers, and automated predictions in the process.

– Viktor, Chief Data Officer.

“What once took ages, we made it real time.”

Adopting artificial intelligence to assess car damages and estimate repairing cost as part of the auto-insurance claim process, the global insurance company shortened not only the time taken to assess damages, but also the overall end-to-end steps for the customers to process a claim, estimate cost, and get their payment. 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% the number of what we describe as “follow up calls” where the customers ask 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.

– Apolline, Director of Digital Transformation.

Lesson Learned

  • Adopting artificial intelligence to inspect car damages in auto-insurance claim processes could significantly reduce efforts and time taken by assessors, speeding up overall claim process
  • Accurate AI in car damage assessment in auto-insurance could result in impactful estimation of repairing cost in real time. To achieve this, training the AI model with accurately labelled data is crucial.
  • Unique solution to build training datasets with data annotation specifically addressed to inspect different types and severity of car damages will greatly improve efficiency and productivity as positive results