1.
AI in Insurance
How to Automate Insurance
Claim Processing with Machine
Learning?

2.
Technology leader with 20+ years expertise in Product Development, Business strategy and
Artificial Intelligence acceleration. Active contributor in the New York AI community
Extensively worked with global organizations in BFSI, Healthcare, Insurance, Manufacturing,
Retail and Ecommerce to define and implement AI strategies
Nisha Shoukath
Co-founder, People10 & Skyl.ai
The Speaker

3.
Shruti Tanwar
Lead - Data Science
Extensive experience building future tech products using Machine Learning and
Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis, full stack development
and building world class products in ecommerce, travel and healthcare sector.
The Speaker

4.
CTO & Software Architect with 15 years of experience working at the
forefront of cutting-edge technology leading innovative projects
Areas of expertise include Architecture design, rapid product
development, Deep Learning and Data Analysis
The Panelist
Bikash Sharma
CTO and Co-founder at Skyl.ai

5.
All dial-in participants will be muted to enable the
presenters to speak without interruption
Getting familiar with ‘Zoom’
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen

6.
A quick intro about Skyl.ai
ML automation platform for unstructured data
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labelling
Easy-to-use & scalable AI SaaS platform

7.
Live Demo
of Smart Claim
Management
...In the next 45 minutes
How organizations
are leveraging AI &
Machine learning in
Insurance
Best practices to
automate machine
learning models
1 2 3

8.
POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business

9.
How organizations are
leveraging AI & Machine
learning in Insurance01

10.
Power users of AI with a
strong digital base can
boost the profits by
1-5% above industry
average.
Mckinsey Insights
“Why a digital base is critical”

11.
How AI is transforming Insurance
Sales &
Marketing
Claim
Management
Risk
Analysis
Customer
Engagement

12.
Enable Sales & Marketing
Focused efforts, Tailored products
⊚ Prospect Pre-qualification
⊚ Relevant product recommendations
⊚ Virtual agents for guided online
buying process
Spixii featured in The digital insurer

13.
Claim Management
Reduce claim settlement
time and increase accuracy
⊚ Car damage recognition
⊚ Healthcare claim settlement
⊚ Anticipate health risks
ICICI Lombard app - Insure

14.
Risk Analysis
Faster fraud identification &
prediction
⊚ Transaction analysis to identify,
predict & prevent fraudulent claims
⊚ Reaffirmation with AI to verify if the
asserted claims are true or not
ICICI Lombard app - Insure

15.
Customer Engagement
Increase customer lifetime
value & satisfaction
⊚ Face recognition & voiceprint to
reduce customer verification time
⊚ Churn prediction & reduction
⊚ Upsell & Cross-sell products
⊚ Use NLP to address queries on policy
Facial Recognition

16.
Smart Claim Management
For Automotive Insurance

17.
20-50 million people
Get Injured in accidents globally
1.25 million people
Die in road crashes every year
$518 billion
Cost accrued globally
Assocition for safe international travel
https://www.asirt.org/safe-travel/road-safety-facts/

18.
Traditional time consuming manual claim process
1 2 3 4 5 6
Claim
Submission
Insurance
payment
Original
receipt
submission
Manual
data
transfer
Claim
assessment
Claim
approval

19.
Car damage recognition solution with Machine Learning
1 2 3 4
Digital Claim
submission
Auto
evaluation
and cost
estimation
Automated
document workflow
guided by Machine
learning system
Insurance
payment

20.
Live Demo of smart
claim management for
automotive insurance02

21.
8 stages of Machine Learning workflow

22.
Live Demo on
Smart Claim Management for
Auto Insurance

23.
POLL #2
State your role in the AI initiatives/ projects in your
organization
⊚ We don’t have any AI projects yet
⊚ Practitioner - Data Science /
Engineering background
⊚ Sponsor/Executive
⊚ Product Manager
⊚ Project Manager
⊚ Student
⊚ Others

24.
Best practices to
automate machine
learning models
03

25.
POLL #3
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms

26.
Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)

27.
Data Labeling - Simple 4 steps process
(collaboration jobs, guided workflow…)

28.
Data Labeling - Real-time early visibility
(class balance, missing data…)

29.
Data Labeling - Early Visibility
(data frequency, data intuition, outliers, trends, labeling accuracy…)

30.
Data Labeling with Effective Collaboration
(Job allocation, trend, statistics, interactive messaging…)
Manage collaborator
progress, activity,
interactive messaging
Analyse trends and progress of
your data labeling job in real
time with statistics and
interactive visualizations

31.
Data Visualization to build strong data intuition
( visuals for data composition, data adequacy)

32.
One click training at scale
(Easy feature sets, out of the box algorithms, API integration, hyper
parameter tuning, auto scaling…)
● Train, Deploy and Version your
models by creating feature-sets
in no time with our easy feature
selection provision.
● Choose from state-of-art neural
network algorithms, tune
hyperparameters and see logs for
your training in real time.
● Integrate our powerful inference
API with your application for
AI-driven actionable intelligence.
● Auto scaling of model training
based on data and
hyperparameters

33.
Model Monitoring of metrics in real-time
(inference count, execution time, accuracy…)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time. No
black boxes here.

34.
Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time. No
black boxes here.

35.
No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
1. No DevOps required - Incorporates automatic
deployment and dockerization
2. Scalable tech with latest stack
3. Domain agnostic build by data type
4. Scalable on demand
5. On premise and saas models

36.
Skyl.ai - as ML automation platform

37.
Try out 15 days free trial with complimentary
consultation on pilot project
Register https://skyl.ai/form?p=start-trial

38.
Questions?
contact@skyl.ai
https://skyl.ai/
?

39.
85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
We hope to hear from you soon
Thank you for joining!