The document discusses the journey from structured to unstructured data in insurance documents. It describes how omni:us uses computer vision, natural language processing, and machine learning to extract key information from insurance documents that may have highly variable layouts and formats. The presentation outlines how omni:us handles structured, semi-structured, and unstructured data through techniques like template alignment, text detection, and supervised learning to provide a scalable solution for insurance companies.

1. Journey from Structured to Unstructured Data
Nischal HP
VP, Engineering and Data Science
@nischalhp

2. 22.11.2018 omni:us @ datanatives 2018 2
Agenda
omni:us
Structured,
Semi-structured and
Unstructured data
Key-takeaways

3. omni:us @ datanatives 2018 3
omni:us vision ?
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4. omni:us @ datanatives 2018 4
We want to supercharge insurance industry with
speed and transparency
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5. omni:us @ datanatives 2018 5
The motivation?
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6. Motivation
6omni:us @ datanatives 2018
Today‘s insurance
is process driven
Tomorrow
insurance is data
driven
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7. 22.11.2018 omni:us @ datanatives 2018 7
What we do .

8. What we do
8
DOCUMENT CLASS
Policy
POLICY NUMBER
H 54/16 307 728
CUSTOMER
Renolate GmbH
10115 Berlin
AGENT
pma Insurance Broker
48149 Nurnberg
RISK DESCRIPTION / INSURED LOCATION
Private liability insurance comfort plus
Dog liability
Environmental damage insurance
Employees on premises
POLICY
Liability Protection
EFFECTIVE DATE OF CHANGE
22.12.2016 12:00
TERMINATION
22.12.2019 12:00
ANNUAL CHARGE
EUR 424,63
COVERAGES
Persons & property damage flat
Financial losses
Environmental damage basic flat
EUR 3.000.000
EUR 100.000
EUR 3.000.000
omni:us understands insurance documents
with highly variable layouts, and extracts
relevant data to radically streamline manual
processing of claims & policy comparisons.
omni:us Claim allows claim settling times
to be a matter of minutes. Not weeks.
omni:us Policy enables insurers to make
quicker, more accurate quotations &
comparisons.
omni:us @ datanatives 201822.11.2018

9. omni:us @ datanatives 2018 9
Who are we ?
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10. Who we are
Company
• Startup founded in 2015
• Berlin based with US subsidiary
• Enterprise customers in 6 countries on 2
continents
• Engineering and Scientific background in
CV/ML/AI
• 40+ team
10
USPs
• Fully scalable & global coverage
• Process-less integration and common
APIs
• Teachable AI
• Pretrained models for a variety of use
cases
nvidia Inception
Program
Red Herring
Winner 2017
Google
AI Program
Plug and Play
Insurtech
Member
omni:us @ datanatives 201822.11.2018

11. omni:us @ datanatives 2018 11
How do we do
this ?
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12. How we do it
12omni:us @ datanatives 2018
Computer Vision Natural
Language
Processing
Traditional ML
Whole lot of
Engineering
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14. omni:us @ datanatives 2018 14
Agenda
omni:us
Structured,
Semi-structured and
Unstructured data
Key-takeaways
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15. omni:us @ datanatives 2018 15
Types of
documents ?
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16. Insurance documents
16
• Types (use cases)
– Claim
– Policy
• Parts (pages)
– Forms
– Invoices
– Reports
– Letters
– Photos
– ...
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17. omni:us @ datanatives 2018 17
Structured
Documents .
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18. European accident forms
18omni:us @ datanatives 201822.11.2018

19. omni:us @ datanatives 2018 19
Ideal world .
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20. Ideal world
20omni:us @ datanatives 201822.11.2018

21. omni:us @ datanatives 2018 21
Real world .
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22. Real world challenges
• Various layouts
• Different versions
• Multiple languages
• Varying quality
• Diverse writings
• Light/shadows
• Misalignments
• Deformations
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23. Processing pipeline
Page classification
Template
alignment
Text
detection
Handwriting
recognition
Data
validation
23omni:us @ datanatives 201822.11.2018

24. omni:us @ datanatives 2018 24
Template
Alignment .
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25. Template alignment
Objective
• Align existing form template to filled
pages
• Ease subsequent information extraction
Method
• Estimate global transformation
• Feature based approach for photos
• Dense pixel based approach for scans
Results
• Precise alignment for 95% of the pages
• Reliable despite drastic appearance
changes
25omni:us @ datanatives 201822.11.2018

26. Template alignment
26omni:us @ datanatives 201822.11.2018

27. Business benefits
27
• Same quality with 66% lower human time /costs
• Flexible deployment in the cloud or on premise
omni:us @ datanatives 201822.11.2018

28. 28
{Semi/Un}
Structured
Documents .
omni:us @ datanatives 201822.11.2018

29. 29omni:us @ datanatives 201822.11.2018

30. 30
From that, Go to
this .
omni:us @ datanatives 201822.11.2018

31. 31
DOCUMENT CLASS
Policy
POLICY NUMBER
H 54/16 307 728
CUSTOMER
Renolate GmbH
10115 Berlin
AGENT
pma Insurance Broker
48149 Nurnberg
RISK DESCRIPTION / INSURED LOCATION
Private liability insurance comfort plus
Dog liability
Environmental damage insurance
Employees on premises
POLICY
Liability Protection
EFFECTIVE DATE OF CHANGE
22.12.2016 12:00
TERMINATION
22.12.2019 12:00
ANNUAL CHARGE
EUR 424,63
COVERAGES
Persons & property damage flat
Financial losses
Environmental damage basic flat
EUR 3.000.000
EUR 100.000
EUR 3.000.000
omni:us @ datanatives 201822.11.2018

32. 32
Templates don’t
scale,
Supervised
learning does .
omni:us @ datanatives 201822.11.2018

33. 33omni:us @ datanatives 2018
Supervised learning
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34. 34omni:us @ datanatives 2018
Supervised learning
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35. 35omni:us @ datanatives 2018
Computer Vision
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36. 36omni:us @ datanatives 2018
Computer Vision
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37. 37omni:us @ datanatives 2018
Multi Dimensional Thought Process
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38. 38omni:us @ datanatives 2018
Natural Language Processing
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39. 39omni:us @ datanatives 2018
Natural Language Processing
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40. 40omni:us @ datanatives 2018
CV, NLP
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Agenda
omni:us
Structured,
Semi-structured and
Unstructured data
Key-takeaways
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42. 42omni:us @ datanatives 2018
Key Takeaways
● Deep Learning to solve tasks and not datasets requires a very strong thought
process and engineering efforts
● Don’t have to be afraid of unstructured data anymore
● Solving a problem, requires multi-dimensional approach. No one tool can solve the
problem entirely.
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44. Thank you for listening!
Any questions?
Web: https://omnius.com/
Blog: https://medium.com/omnius
Email: nischal@omnius.com
Twitter:@nischalhp

45. Image References
● https://jaywalker-digital.ch/wp-content/uploads/2017/06/data-flow-process.p
ng
● http://www.healtheconomics.com/blog/wp-content/uploads/2018/01/3.png