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Success story
Outsource People 2016
April 2016
Outline
3
Facts about me
What problems banks are trying to solve?
What prevents them to do that?
Smart Process Automation
Team Collaboration
Q&A
Facts about WorkFusion
Facts about me
4
BSU. Faculty of Mechanics and Mathematics. 1995 – 2000
NT lab – 2 years. VHDL developer
IBA – 8 years. Developer -> Team Lead
Exadel – 4 years. Department Manager
Strevus – 3 years. Co-Founder. Director of Engineering
WorkFusion – 1 year. Co-Founder. Director of Engineering
Institute of Technical Cybernetics, National Academy of Sciences
Facts about WorkFusion
5
 WorkFusion is in 10 NYC Startups That Raised the Most Amount of
Capital in December
 WorkFusion Raises $14M to Drive Smart Process Automation in
Enterprise
What problems banks are trying to
solve?
6
 Reduce cost
 Configurability
 Automation process
 Reduce FTE: 25% or more
What prevents them to do that?
7
 Tech team can not deliver
 Leaving Legacy systems as is
 Huge amount of low quality documents
 All optimization was considered done by moving to Offshore
 Support Chinese, Korean, Japanese, etc.
Smart Automation Process
April 2016
What is “Robotics” and “Cognitive”?
Why are they new?
9
Head work
Hand work Cognitive
Automation
Robotics
“aka” RPA
i.e. entering data from one
application into another
i.e. extracting information from
unstructured documents
Why now?
End of labor arbitrage
+ strong adoption of
self-service across
enterprise
Why now?
Breakaway progress in
AI tech + availability of
data and compute in
cloud
50% impact can be expected from full-stack
implementation of smart automation, as high
as 70% if starting from onshore
10
Initial state Future state
Plain old
offshoring and
outsourcing
Sourcing CognitiveRobotics
10-15%
robotic
automaton on
offshore, 40%
on onshore
resources
10-20%
cognitive
automation on
top of robotics
5-10%
human worker
analytics / UX
improvements
30%
+50%
Smart
automation
Automation of
onshore FTE
remaining due to
regulatory reasons
WorkFusion is Smart Automation
11
Human-in-
the loop
Crowdsourcing Statistical
Quality Control
2011
MIT CSAIL lab research leads to R&D
on human-in-the-loop computing
Microtasking Robotics
2012
WorkFusion launches first SaaS platform
for Microtasking in enterprise
Machine
Learning
2013
WorkFusion launches Machine Learning
automation
Smart
Automation
2014
WorkFusion becomes first full stack
robotics + cognitive + human platform
Full stack
Automation
2015
WorkFusion patents Worker Fitness,
Virtual Data Scientist
CASE STUDY
Processing of Invoices to extract header information and
individual line-items
12
Situation
 A Human Resource software
company processes up to 150k
invoices on a monthly basis
 The current processing method is
fully manual, which limits the amount
of information that can be extracted
Approach
 Optical Character Recognition
(OCR) capabilities are applied to
turn each invoice into structured text,
which is then passed through a
workflow
 Machine Learning models are
applied to automatically extract
values where possible
 When human effort is required, a
semi-automated information
extraction task is used to speed-up
the manual work
Impact
FULLY MANUAL
TO
80%
AUTOMATION
1 LINE PER
DOCUMENT TO
ALL DOCUMENT
LINES
KEYING
REPLACED BY
HIGHLIGHTING
AND AUTO-
SELECTION
CASE STUDY
Standardizing on processing format to collect key values
from tax documents
Problem
 Customer is storing and analyzing
tax documents for its customers. To
add each document to the database
correctly, the Company Name and
Jurisdiction need to be extracted
from the document. The current
process is fully manual due to the
large variety of PDF types and tax
documents
Impact
 Utilized WF’s OCR technology to
convert all documents to a format
maintaining context. Configured web
scraper and information extraction
tools to be able to collect data.
Incorporate in a workflow that
incudes normalization of values and
human exception management.
1
How does WorkFusion enabled enterprise automation architecture?
14
Enterprise Infrastructure / Enterprise Cloud
Systems of Record (ERP, CRM, …)Data Warehouse / Data Lake
Cognitive Automation (VDS)
Digitization (OCR, Scraping, …)
Workforce Orchestration
AutomationAutomationenablers
Mobile
Business Process Management
Messaging UXDataIntegration(ETL,DQ,MDM)
Robotic Automation (RPA)
AutomationBI/Analytics
EmbeddedAutomation
Team collaboration
June 2015
Team structure
Sales, marketing
Pre-Sales
R&D
Engineering
Professional Services
Customer Support
16
Process Improvements
Top priorities focus
Feature Team Approach
Continuous Delivery
Collaboration
Ownership
Seniority
Expertise
17
Q&A
18
Contact
19
Ilya Kazimirovskiy
Director of Engineering
Mobile: +375 29 163 3393
Email: ikazimirovskiy@workfusion.com
Site: workfusion.com

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Ilya Kazimirovskiy, Outsource People_2016_Minsk

  • 2. Outline 3 Facts about me What problems banks are trying to solve? What prevents them to do that? Smart Process Automation Team Collaboration Q&A Facts about WorkFusion
  • 3. Facts about me 4 BSU. Faculty of Mechanics and Mathematics. 1995 – 2000 NT lab – 2 years. VHDL developer IBA – 8 years. Developer -> Team Lead Exadel – 4 years. Department Manager Strevus – 3 years. Co-Founder. Director of Engineering WorkFusion – 1 year. Co-Founder. Director of Engineering Institute of Technical Cybernetics, National Academy of Sciences
  • 4. Facts about WorkFusion 5  WorkFusion is in 10 NYC Startups That Raised the Most Amount of Capital in December  WorkFusion Raises $14M to Drive Smart Process Automation in Enterprise
  • 5. What problems banks are trying to solve? 6  Reduce cost  Configurability  Automation process  Reduce FTE: 25% or more
  • 6. What prevents them to do that? 7  Tech team can not deliver  Leaving Legacy systems as is  Huge amount of low quality documents  All optimization was considered done by moving to Offshore  Support Chinese, Korean, Japanese, etc.
  • 8. What is “Robotics” and “Cognitive”? Why are they new? 9 Head work Hand work Cognitive Automation Robotics “aka” RPA i.e. entering data from one application into another i.e. extracting information from unstructured documents Why now? End of labor arbitrage + strong adoption of self-service across enterprise Why now? Breakaway progress in AI tech + availability of data and compute in cloud
  • 9. 50% impact can be expected from full-stack implementation of smart automation, as high as 70% if starting from onshore 10 Initial state Future state Plain old offshoring and outsourcing Sourcing CognitiveRobotics 10-15% robotic automaton on offshore, 40% on onshore resources 10-20% cognitive automation on top of robotics 5-10% human worker analytics / UX improvements 30% +50% Smart automation Automation of onshore FTE remaining due to regulatory reasons
  • 10. WorkFusion is Smart Automation 11 Human-in- the loop Crowdsourcing Statistical Quality Control 2011 MIT CSAIL lab research leads to R&D on human-in-the-loop computing Microtasking Robotics 2012 WorkFusion launches first SaaS platform for Microtasking in enterprise Machine Learning 2013 WorkFusion launches Machine Learning automation Smart Automation 2014 WorkFusion becomes first full stack robotics + cognitive + human platform Full stack Automation 2015 WorkFusion patents Worker Fitness, Virtual Data Scientist
  • 11. CASE STUDY Processing of Invoices to extract header information and individual line-items 12 Situation  A Human Resource software company processes up to 150k invoices on a monthly basis  The current processing method is fully manual, which limits the amount of information that can be extracted Approach  Optical Character Recognition (OCR) capabilities are applied to turn each invoice into structured text, which is then passed through a workflow  Machine Learning models are applied to automatically extract values where possible  When human effort is required, a semi-automated information extraction task is used to speed-up the manual work Impact FULLY MANUAL TO 80% AUTOMATION 1 LINE PER DOCUMENT TO ALL DOCUMENT LINES KEYING REPLACED BY HIGHLIGHTING AND AUTO- SELECTION
  • 12. CASE STUDY Standardizing on processing format to collect key values from tax documents Problem  Customer is storing and analyzing tax documents for its customers. To add each document to the database correctly, the Company Name and Jurisdiction need to be extracted from the document. The current process is fully manual due to the large variety of PDF types and tax documents Impact  Utilized WF’s OCR technology to convert all documents to a format maintaining context. Configured web scraper and information extraction tools to be able to collect data. Incorporate in a workflow that incudes normalization of values and human exception management. 1
  • 13. How does WorkFusion enabled enterprise automation architecture? 14 Enterprise Infrastructure / Enterprise Cloud Systems of Record (ERP, CRM, …)Data Warehouse / Data Lake Cognitive Automation (VDS) Digitization (OCR, Scraping, …) Workforce Orchestration AutomationAutomationenablers Mobile Business Process Management Messaging UXDataIntegration(ETL,DQ,MDM) Robotic Automation (RPA) AutomationBI/Analytics EmbeddedAutomation
  • 16. Process Improvements Top priorities focus Feature Team Approach Continuous Delivery Collaboration Ownership Seniority Expertise 17
  • 18. Contact 19 Ilya Kazimirovskiy Director of Engineering Mobile: +375 29 163 3393 Email: ikazimirovskiy@workfusion.com Site: workfusion.com