IMAGE PROCESSING AND OBJECT DETECTION APPLICATION: INSURANCE CASE - ABOUT CLAIMS AND UNDERWRITING

1. IMAGE PROCESSING AND OBJECT DETECTION APPLICATION:
INSURANCE CASE - ABOUT CLAIMS AND UNDERWRITING
Dr. Volkan OBAN1
1Asst. Prof., Istinye University, İstanbul TÜRKİYE
Abstract
In this study, some image processing and object detection techniques are handled for claims
processing and underwriting. Image detection and recognition provides different perspectives in
the insurance industry, especially in the demand process, as well as in traditional practices.
Insurers endeavor and concentrate on determining whether the claims are valid or not. The use
of image detection techniques allows realistic assessment of images of damaged objects loaded
and their claims by policyholders.
Keywords: Artificial Intelligence, Computer Vision, Image Processing, Object Detection, Deep
Learning, Insurance, Tensorflow, Keras, Imageai,Yolo
1. Introduction:
The insurance data contains various types of
information in the form of continuous or
discrete numbers, short texts, huge
paragraphs, and, of course, images.
Multitudes of claims, customer inquiries and
masses of data make the insurance industry
an ideal address for artificial intelligence and
cognitive technologies.
Artificial intelligence is already deeply
embedded in insurance processes. A 2017
study reports that the insurance industry has
invested $124 million in AI, compared to an
average of $70 million invested by other
industries.
According to McKinsey’s Insurance 2030
Report, with the new wave of deep learning
techniques, such as convolutional neural
networks, AI can truly mimic the perception,
reasoning, learning, and problem solving of
the human mind. AI will help the insurance
sector shift from its current state of “detect
and repair” to “predict and prevent.” While AI
has made great strides within the insurance
industry, one promising element of a robust
AI strategy must be further explored:
computer vision technologies.
Implementing computer vision technology
that enables visual insurance claims has
emerged as a solution that can transform the
entire claims process. Computer vision
guides customers through the process of
capturing visuals while recognizing objects
within those images, classifying the images,
and routing the enquiries to the to the right
agents or adjusters for immediate analysis
and incident assessment
By embedding AI within claims processes,
insurers can easily access and extract the
relevant data and reduce processing times.
AI can also identify patterns in masses of
data and help detect fraudulent claims
during the process. Using machine learning

2. capabilities, AI can automatically calculate
past damages and predict the costs from
historical data, aiding in the determination of
premiums. McKinsey predicts that as
insurers and their ecosystem become more
adept at using AI technologies to enhance
decision making and operations, lower costs,
and optimize the customer experience, the
pace of adoption will further accelerate.
While AI has made great strides within the
insurance industry, one promising element
of a robust AI strategy must be further
explored: computer vision technologies.
Computer vision is a field that works on
enabling computers to see, identify, and
process images/videos like a human being. It
is like imparting human intelligence and
instincts to a computer. It may sound
promising; however, in reality, it is quite
difficult to enable computers to recognize
images of different objects. It is not a new
field, but it has caught more attention and
become more precise only after the advent of
deep learning in the last decade.
[InsurAnalytics] Computer vision is based
strongly on artificial intelligence, as the
computer must gain high-level
understanding from digital images.
Some application of computer vision in the
insurance industry.
 IMAGE SIMILARITY
 FACIAL RECOGNITION
 OBJECT DETECTION AND
RECOGNITION
 MOTION ESTIMATION
 DAMAGE DETECTION
……, etc.
Positive Acquisitions of Image
Processing Technologies to Insurance
Sector:
 The use of facial and image
recognition technologies, as well
as images stamped with geo-
location and time, enables
insurance companies to reduce
their risk of fraud.
 Computer vision’s ability to
validate claims in real-time
enables expedited claim
settlements
 Computer vision improves KPIs
across the board, which
translates into significant cost
reductions in contact centre from
improved FCR and AHT, reduced
adjuster time in the field, and less
customer churn.
 With a faster AHT and increased
FCR, insurance companies can
validate and process claims
faster than ever without the need
for staff augmentation.
 The visual upload process is fast,
interactive, and eliminates the
need to wait for a field adjustor
or live agent to approve the
claim. When claims are
processed faster, customers are
happier. [TechSee] and
[Andrew Mort]
Fraudulent claims
It has been commonly observed that many
people tend to file fraudulent claims. For
instance, people filing health insurance
claims multiple times and across multiple
insurance companies. Similar is the scenario
for motor insurance – frauds are increasing
at an alarming rate. As per a report by
economic times, “a combination of poor due
diligence in writing policies by insurance
companies and the organizational
efficiencies of criminals in identifying those
who are on deathbed and in enlisting doctors
to produce fake certificates led to frauds
which are estimated

3. to have cost over Rs 10,000 crore annually to
the industry.” These frauds directly affect the
profitability of the industry. In order to make
up for the fraudulent claims, companies tend
to raise premiums, meaning innocent
customers must pay higher amounts of
premiums.
THREE WAYS COMPUTER VISION IS
CHANGING P&C INSURANCE [Capco]
Computer vision, a field of artificial
intelligence (AI), has developed rapidly over
the last ten years, with the end goal of a
computer learning to see as a human would.
The application of computer vision has the
potential to transform the property and
casualty insurance (P&C) industry, by
helping individuals and insurance
companies throughout virtually all the stages
of the underwriting and claims life cycles.
Read on as we examine three scenarios that
highlight how computer vision can enhance
safety, accuracy and efficiency throughout
the insurance process.
2. IMAGE SIMILARITY
Image Similarity compares two
images and returns a value that tells
you how visually similar they are.
Here is used hashing algorithms by
Python. (hashlib module- import
hashlib).
The lower the the score, the more
contextually similar the two images
are with a score of ‘0’ being identical.

4. 3. FACIAL RECOGNITION
Recognize and manipulate faces from
Python. Built using dlib’s state-of-the-art face
recognition. Built with deep learning. The
model has an accuracy of 99.38% on the
Labeled Faces in the Wild benchmark.
Face Detection:
Face detection is a computer vision
technology that helps to locate/visualize
human faces in digital images. Face detection
has gained a lot of importance especially in
fields like photography, security, and
marketing.
4.OBJECT DETECTION AND
RECOGNITION
Object detection is the craft of
detecting instances of a certain class,
like animals, humans and many more
in an image or video Object Detection
(Tensorflow models in Python)
makes it easy to detect objects by
using pretrained object detection
models

5. number of person(insan sayısı) : 13
number of car(arac sayısı) : 8
number of truck(kamyon sayısı) : 2
number of bus(otobüs sayısı) : 1
number of backpack(sırt çantası sayısı) : 1
Is there any ambulance at the scene of the accident?
ambulance : 82.00276494026184
police van: 15.833474695682526
minibus: 1.516808196902275
Garbage truck: 0.15010888455435634
fire engine : 0.11310353875160217
moving van: 0.08072283817455173
trolleybus : 0.07773929974064231
snowplow : 0.04561058012768626
tow truck : 0.037757077370770276
crane : 0.024561924510635436

6. number of person(insan sayısı) : 20
number of car(arac sayısı) : 12
number of truck(kamyon sayısı) : 1
number of bus(otobüs sayısı) : 0
number of traffic lights(trafik lambasısayısı) : 2
Are there any traffic sign and lights?
number of person(insan sayısı) : 1
number of car(arac sayısı) : 4
number of truck(kamyon sayısı) 2
number of traffic lights(trafik lambası sayısı) 3

7. number of pedestrian(yaya sayısı) : 6
number of person(insan sayısı) : 12
number of car(arac sayısı) : 4
number of truck(kamyon sayısı) 1
number of traffic lights(trafik lambası sayısı) 0

8. Car Speed Detection
5.Conclusion:
Claims processing is a key touch point in the
customer life cycle, and insurers have only a
short opportunity to wow or disappoint their
policyholders. With much at stake, insurers
must adopt innovative tools and
technologies in order to transform the claims
function. Implementing computer vision
technology that enables visual insurance
claims has emerged as a solution that can
transform the entire claims process.
Computer vision guides customers through
the process of capturing visuals while
recognizing objects within the images,
classifying the images. Artificial intelligence
(computer vision, image processing …)
techniques have been studied for the current
situation in the industry.