kili-technology.com Open in urlscan Pro
2606:4700:20::681a:360  Public Scan

Form analysis
0 forms found in the DOM

Text Content

Watch the replay!
Fast Track Shipping Insurance AI Models: Overcoming Training Data Challenges
 * Products
   Platform
    * Labeling
    * Quality
    * Integration
    * LLM Fine Tuning
    * LLM Evaluation & Testing
   
   
   Labeling Services
    * Kili Simple Offer
    * ML expert guidance
   
   
   Assets
    * Text Annotation Tool
    * Image Annotation Tool
    * Video Annotation Tool
    * OCR Annotation Tool
    * Geospatial Annotation Tool
   
   
   
   Master the craft of preparing training data to turbocharge your ML efforts
   
   DOWNLOAD EBOOK HERE >
 * Solutions
   Solutions
   Data LabelingText AnnotationNatural Language ProcessingComputer Vision
   Image AnnotationVideo AnnotationLLM EvaluationRAG Evaluation
   
   
   Use Cases
    * Insurance
    * Security
    * Healthcare
    * Manufacturing
    * Content categorization
   
   
   
   Master the craft of preparing training data to turbocharge your ML efforts
   
   DOWNLOAD EBOOK HERE >
 * Company
    * About us
    * Why Kili
    * Careers
    * Events
   
   
 * Resources
    * Blog
    * Events & Webinars
    * Whitepapers
    * Case Studies
    * Open Datasets
    * Models
   
   
   
   Checklist: Comparing Data Labeling Services
   
   Download our free resource here
 * Docs
    * What is Kili Technology?
    * Getting started
    * Changelogs
   
   
   
   Users & rolesHandling projectsLabelingQuality Management
   PluginsAutomationKili APITroubleshooting
   
   
 * Pricing

 * Request a demo

Get My Data LabeledLog In
 * Products
 * Solutions
 * Company
 * Resources
 * Docs
 * Pricing

 * Request a demo

Get My Data Labeled
 * Home
 * /
 * Case studies
 * /
 * Insurance - Transforming car damage assessment in auto-insurance claim
   process


INSURANCE - TRANSFORMING CAR DAMAGE ASSESSMENT IN AUTO-INSURANCE CLAIM PROCESS


Industry
Insurance
Uses cases
Auto-insurance Claim Processing



IMPACT




CHALLENGES

 * 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


SOLUTIONS

 * 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


CONTEXT

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.


HOW IT ALL STARTED

When AI speeds up the assessment of car damage and repairment cost

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.

Their objectives :

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.




CHALLENGE

The challenges of building an 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 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.

Apolline
Director of Digital Transformation

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.


ISSUES WITH COMPLEX DOCUMENT PROCESSING IN INSURANCE

 * Multiple docs in a single PDF

 * Many possible variations: in the location of the data, between sources

 * Complex data : Complex layouts
   Nested tables, Handwriting, Multiple docs in a single PDF, Symbols, Images,
   logos,


SOLUTION

A versatile training data platform to annotate extensive amount of unstructured
images

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.

Viktor
Chief Data Officer

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.


IMPACT

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

Adopting artificial intelligence to assess car damages and estimate repair costs
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% 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.

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 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

Sign Up

Talk to an expert


RELATED RESOURCES



Blog

AI FOR COMPLIANCE: WHAT, WHY AND HOW?



AI For Compliance: What, Why and How?
Whitepaper

KILI’S GUIDE TO THE NEW AI PARADIGM



Kili’s guide to the new AI paradigm
Tutorial

OPINION CLASSIFICATION WITH KILI & HUGGINGFAC...



Opinion Classification with Kili & HuggingFac..


;


GET STARTED

Get started! Build better data now.

Sign UpTalk to an expert

Kili Technology © 2023

Products
LabelingQualityIntegrationProfessional ServicesPlans & Features
Tools
LLM Fine-Tuning ToolLLM Evaluation ToolImage Annotation ToolVideo Annotation
ToolNLP Text Annotation ToolOCR Annotation ToolGeospatial Annotation ToolData
Labeling Tool
Guides
Data Labeling GuideRAG Evaluation GuideLLM Evaluation GuideText Annotation
GuideNatural Language Processing GuideComputer Vision GuideImage Annotation
GuideVideo Annotation Guide

Kili Technology © 2023

CompanyPress
France47 boulevard de Courcelles, 75008 Paris
United States524 Broadway, New York, NY 10012
PRIVACY POLICYLEGAL NOTICESECURITY INFOSTATUS










This website uses cookies
Hey! At Kili Technology, we are committed to ensuring your privacy and providing
you with the best possible experience. Now, before you jump into exploring our
fantastic content, we'd like to get your permission to use these cookies. Don't
worry; we've got your privacy covered! 😊

Our cookies serve two primary purposes:

1️⃣ Enhancing Your Experience: These cookies allow us to remember your
preferences so you don't have to set them every time you visit.

2️⃣ Analyzing and Improving: We use cookies to enhance our content, features,
and overall user experience.

But here's the best part: we respect your choices! You have full control over
which types of cookies you want to enable or disable. If you accept, we will use
cookies for both the aforementioned purposes. However, if you prefer not to, we
will only use the necessary cookies required for the site's basic functionality.
Read more
Save & Close
Yes, it's Ok for me
Let me choose Hide details