The document discusses the implementation of intelligent document automation and machine learning in the insurance industry to enhance workflow efficiency and improve document processing. It highlights the significant benefits of automation, such as reducing claims processing time from weeks to hours, achieving an 84% reduction in processing time, and allowing legal professionals to focus more on their core responsibilities. Additionally, it emphasizes the advantages of a cloud-based solution that can be deployed rapidly with real-time updates on AI recognition technology.

1. Intelligent Document
Automation and
Machine Learning
for insurance
industry
Workflow automation and improvements in
document processing and AI/ML powered
insurance use-cases
February 2023

2. Introduction
2
Muntis Rudzitis
LEAD DATA SCIENTIST,
EMERGN

3. EMPLOYEES
700+
OFFICES
ENTERPRISE
CUSTOMERS
265+
ESTABLISHED
IN 2009
OF CURRENT
CLIENTS HAVE
BEEN WITH US
FOR MORE
THAN 3 YEARS
73%
OF REVENUE
IS REPEAT
BUSINESS
IS REFERRAL
BUSINESS
67% 83%
COUNTRIES
(UK, US, IRE,
RU, LV, UKR, POR)
8
12
2009
OUR SERVICES
DELIVERY
CONSULTING
LEARNING

4. • Data communication
• Document life-cycle & automation
• Case study
• Pain points
• Intelligent automation
4
Private and confidential

5. Data communication
5
Touch points between clients, companies and services
Mostly unstructured data in forms
• Internal systems
• Direct integration
• Data storage
• Reporting / DWH
COMPANY ALPHA
• E-mails
• PDF
• Office documents
• Chatbots
Internal data communications -
structured data
Direct integrations / services -
structured / semi-structured data
External data communication -
mostly unstructured data in forms
Mostly unstructured data in forms
• Internal systems
• Direct integration
• Data storage
• Reporting / DWH
COMPANY BETA
• E-mails
• PDF
• Office documents
• Chatbots
• Service / external integrations • Service / external integrations

6. Automation of the document flow
DOCUMENT LIFE-CYCLE
RECEIVE
DOCUMENT
PROCESS
DOCUMENT
ARCHIVING
• Highly manual
• Need decision making, involving knowledge worker to do manual tasks
• Text comprehension (SME)
• Fraught with errors
• Difficult to research/go back, to find things
• Time consuming
• Not possible when scale is large
6

7. Automation of the document flow
DOCUMENT LIFE-CYCLE
RECEIVE
DOCUMENT
SAVE
DOCUMENT
EXTRA
META DATA
7
DOCUMENT
ROUTING
DOCUMENT
PUBLISHING
ARCHIVING
Document and
form recognition
using OCR
Be physical
document or email
or video any format
can be input
Meta-data
extraction
• Key fields
• Key words
• In text
• e.g. PO #
• Subject
• Amount
Document
classification
and routing e.g.
• Classification
• Invoice routing
• Approval,
level, direct
forward to
finance
Document
anonymization
and masking e.g.
Bank and
personal info
Document
archiving by
classification e.g.
Archiving based on
classification: in
cloud or on-site
HOT WARM COLD

8. Case study
Insurance claim processing

9. Pain points in claims processing
9
Structuring data as early as possible
DECISION-MAKING
PROCESS
CUSTOMER
INTERACTIONS
INTERACTIONS WITH
OTHER COMPANIES
Mostly unstructured data in forms
• First notification of loss
• Medical form
• Power of attorney
• Cancelation letter
• Registration form
• Correspondence
• Invoices
• Payment authorization
• Tax documents
• Proof of insurance
• Correspondence
COMPANIES ARE SPENDING
MILLIONS TO RE-KEYING DATA
DECISIONS
POLICY
DOCUMENTS
OWN DATA
SOURCES
EXTERNAL
DATA SOURCES
Workflow process - structured data
INSURANCE COMPANY
Mostly unstructured data exchanges
CLAIMANTS OTHER PARTIES

10. Intelligent claims automation process
10
Incoming documents
related to claims case CLAIMS OPERATOR LAWYER
AI
• Intelligent OCR
• Data extraction
• Data validation
• Document mapping
• Document classification
ALGORITHMIC VALIDATION
IA
• Low-Code workflow
• Low-Code integration
• Rule-based routing
• Form preparation
• Data/document mapping
HUMAN VALIDATION HUMAN DECISION
UNSTRUCTURED DATA
Document types
• Legal
• Financial
• Forms
• Invoices
• Correspondence
CLAIMS SYSTEM
CLAIMANTS AND
OTHER PARTIES

11. ML train / retrain interface
11
Private and confidential
• Microsoft Form Recognition
• Amazon Rekognition Custom Label
• Google Document AI
• Few examples of
form
• Form categorization
in next step
• REST API
FORM TRAINING
DOCUMENTS TRAINED MODEL

12. UX to enable better
performance
12

13. Benefits of claims
automation
This Intelligent Automation approach could reduce
average admin handling time from 12.5 to 2 minutes.
A 84% reduction in processing time.
Handling 500 claims per day with team of 15 employees
you can save about 55 hours per working day.
If you have 1000 claims per month – you can get quick
ROI with intelligent claims automation.
13
Time spent on processing new claims will be
reduced from weeks to less than few hours,
reducing costs and also enabling the team
to quickly add new business opportunities.
It will also completely remove the burden of
administration from lawyers – freeing them
up to focus entirely on legal work.
With a cloud-based approach the
solution could be deployed in 6 weeks
Using cloud-based industry leading Pre-trained AI
for form recognition you use best in class always
updated for recognition and intelligent OCR engine
Low-Code workflow and integration tool help to
automate file transfer and internal/external
correspondence with minimal development effort

14. 14

15. Muntis Rudzitis
muntis.rudzitis@emergn.com