MongoDB World 2019: From Transformation to Innovation: Lean-teams, Continuous Delivery, and Artificial Intelligence with MongoDB at Travelers Insurance

From Transformation to Innovation
Lean Teams, Continuous Delivery and Artificial Intelligence with MongoDB at Travelers Insurance
© 2019 The Travelers Indemnity Company. All rights reserved. 1

Introduction
Michael Braasch
2nd Vice President
Travelers Business Insurance
@mbraasch
Jeff Needham
Lead Architect
@jeffneedham707
2
Doug Calegari
Lead Architect
© 2019 The Travelers Indemnity Company. All rights reserved.

Agenda
• Transformation to Innovation – Minimal Viable Innovation at our core.
• Our Story
• mongoDB as the enabler of innovation
• Use Cases
– Legacy Modernization
– AI/ML
– Business problem solved
– How mongoDB was integral to them

Innovation
Leverage new technology.
Anticipate how customers, agents and brokers will
access and interact with our products and services.
Redesign the way we manufacture and sell our products
and services to improve our productivity and efficiency.

IT at the Front Lines of Innovation
Our Technology choices are the differentiators…
IT is at the front lines of innovation and transformation.
Chance favors the prepared!

Disruptions – Maximize Everything
Build Minimal Viable Innovation Into Your Culture
Imitate characteristics of successful disruptors.
oReinvent and Self-disrupt. Never settle.
oNot all disruptions take hold.
oSmall wins build momentum.
The Runway to Innovate is Almost Always Short.
oGive your people the tools and environment to innovate.
oThere may be naysayers – thank them!
oSeek out and reduce the things that slow you down.

Our Story: 2012 – Where We Started
Problem:
• Policies issued in over a
dozen different RQI’s (Rate-
Quote-Issue systems).
• Data not shared between
RQIs.
• Over a dozen user
experiences required to view
entire book of business for an
account.

Our Story: 2013 to 2015 – Business Insurance Workstation
Solution:
• Build a new web-based
application.
• Use ETL to federate the
data from the legacy apps
into a single, operational
datastore.
• Provide a single user
experience to view (read-
only view of) accounts,
policies and submissions.
BI Workstation Application
LEGACY DATA SOURCES
Web and Application Server
Operational Data Store
RDBMS
Account Executive
Account Manager
ETL

Our Story: From Monolith to Microservices (2015-2017)
Solution:
• Functionality split into
Domains (Bounded-
Contexts).
• Bounded contexts have
exclusivity to their databases.
• Reduce dependencies.
• Reduce complexity.
• Move towards small.
• Continuous Delivery.
• Still in RDBMS

Our Story: From Relational to MongoDB
• We started small.
• It was disruptive.
• Significant reduction in complexity and cost.
• Transformation in the first year alone was un-paralleled.
• THE database of choice for hundreds of Microservices and applications –
across Travelers.
Release 1 Database Objects Release 2 Database Objects Release 3 Database Objects

Our Story: Lean Teams and Continuous Delivery
Increase Flow
Reduce Lead Time
Faster Feedback Loops
Reduce Risk & Disruptions for Ops
Simplify & Optimize
Lean Product
Management
Achieving the effectiveness of Lean Product Management requires,
alignment from both “Business” & “Technology” value streams
BUSINESS
Domain Driven
Development
ARCHITECTURE
Modularity
(Micro Frontends,
Microservices, and
Event/API based apps)
Ownership &
Accountability
DEVELOPMENT TESTING
Automation
Self Service
CD Pipeline
Enablement with
Blue Green and
Canary Deployments
DEVOPS PRODOPS
Monitoring
&
Alerting

Our Story: Lean Teams
12© 2019 The Travelers Indemnity Company. All rights reserved.
Scrummaster
Product Owner
Business Analyst
Developers
Quality Analysts
Typical Agile Team Travelers Lean Team

0 1 2 5
294
500
2012 2013 2014 2015-2018 2018 2019
(estimated)
PRODUCTION DEPLOYMENTS
Our Story: Continuous Delivery

Use Cases: A Year of Accelerated Innovations
2017 – 2018 à Disruption to Transformation:
o Significantly reduce time from business ask to production delivery.
o We realized this goal with the adoption of mongoDB.
2018 – 2019 à Transformation to Innovation:
mongoDB enabling innovations not otherwise possible
o Legacy Modernization
o AI / ML
o Business problem solved
o How mongoDB was integral to each

15
Legacy Modernization: Content Management
Problem:
Legacy Content Management Solution
• Everything is expensive
– Ownership
– Maintenance
– Change
• Fixed schema
– SQL-based metadata tables.
– 100 new doc types, each w 10 unique search
fields = 1000 new SQL fields.
– 100 new database objects.
– Complexity rolls up hill – data to services to
business logic layers.
LEGACY CONTENT MANAGEMENT SOLUTION
PRESENTATION LAYER
BUSINESS LOGIC LAYER
SERVICES LAYER
DATA LAYER
RDBMS
FILE
SYSTEM

Legacy Modernization: Business Insurance Documents
16
Solution:
BI Documents
• Polymorphism!
• mongoDB FTW!
• Radically simplified storage of metadata.
• Significant reduction in complexity at all layers.
• 10:1 reduction in cost of ownership.
The disruption took hold:
• Started with 1 product line.
• Solution deployed to all of BI in the first year.
• Expanding to other Travelers Lines.
MongoDB
api
Search
Metadata
Network File
Storage
Document
Upload
BI Documents Solution
MongoDB
api
Search
Metadata
Network File
Storage
Document
Upload

Cognitive Search Engine
17
Problem:
Knowledge Management
• Time is critical when bringing in new
business
• Cognitive load on underwriters is high
– Manual process for getting information
– Too many systems
– Spend more time training others how to
find info than making decisions
– Info is in various forms structured and
unstructured
• Tacit Knowledge: How were similar
accounts handled?
Legacy Knowledge Repositories
Agents SME’S
Underwriter
?
Managers

18
Solution:
• Index content across multiple sources.
• Predict the source(s) that the user wants
to search based on search term.
• Render the paragraph or sentence that
best matches the question.
Search Interface
Multi-
Classification
Algorithm
Semantic
Similarity
Algorithm
Lucene Indices
What data
source(s) best
match my query?
Which document has
the most relevant
information that
answers my query?
Outcomes:
• Underwriters are unanimously impressed
with the system.
• Reduces the amount of systems the
need to interact with to find information.
• Significantly reduces time to find
information.

mongoDB and Data Science – What is Data Science?
19
60%

mongoDB and Data Science – Data Pipeline
Document Text and
Metadata Staging
(mongoDB)
Model
Training
Preprocessing
Services
Document Retrieval and
Conversion Services
Repository Crawler Services
Feature
Engineering
Training Sets and
Word Embeddings
(mongoDB)
Production Search
Data
(mongoDB)
• Data Preprocessing Pipeline (Feature Engineering, Model Training and Word Embedding Persistence)
• Production Data Ingestion Pipeline
Lucene Indices
• Backend Repository Pipeline

Service Center Automation
Problem:
Service Center Efficiency
• Agents who work with the Travelers send over 1M emails per year.
• These emails are manually categorized by endorsements, cancelations, quotes, etc.
• The process to open the email, understand its intent, triage, route and create a work
task is expensive.
TM
Triage
Route &
Assign
Agent AE/AM CS&S
Hours
Identify Endorsement Type
Identify Business Unit
Manually Keys in Data
(approximately 18 fields needed
for routing in TM

22
Service Center Automation
Solution:
Service Center Automation driven by Artificial Intelligence
• Leverage existing microservices architecture.
• Classification Algorithms to categorize email type and intent.
• Entity Extraction Algorithm to identify key parameters to create a Work Management Task.
Process Email
Content
(extract, OCR)
Classify
Content
Extract Key
Entities
Create WM
Tasks
ACORD PDF IMG
Classify Content
API/Service
Classify Content
using AI Model
Business
API/Services
Data enrichment from
business services
BI WM
API/Service
Create Work Items incl
Route/Assign
Seconds

mongoDB and Data Science – Transactional AI
23
Model Performance Log
(mongoDB)
Model Performance
Evaluation and
Training
Data Scientist
Ops
Recon Service
Risk: Data Drift
• Reconciliation Pipeline
• Alerts when drift is detected
• Retrain Models
Process Email
Content
(extract, OCR)
Classify
Content
Extract Key
Entities
Create WM
Tasks
ACORD PDF IMG
Classify Content
API/Service
Classify Content
using AI Model
Business
API/Services
Data enrichment from
business services
BI WM
API/Service
Create Work Items incl
Route/Assign
Work
Management
System

mongoDB and Data Science – R&D Algorithm Evaluation
Email Classification (Class and Subclass)
– TextCNN (Chosen Model)
– ELMO
– OpenNMT
Entity Extraction
– TextCNN
– OpenNMT
– ELMO
– Flair
– BERT (Chosen Model)
24
mongoDB
Emails via
Document Service
Email OCR Service
Training Set
Generators
Data Science DashboardData Labeling
Interface
• Raw Data
• Cleaned, Labeled Data
• Training Sets
• Model Performance Metrics

MongoDB and Data Science – Key Synergies
25
• Build sophisticated data pipelines
• Mongo and Python
• Manage complex NLP data structures
• State of the art word embedding management

Questions and Wrap-up
• Our story is a typical story.
• Embrace a culture of experimentation in order to innovate.
• Explore new technologies – mongoDB has been an excellent enabler.

MongoDB World 2019: From Transformation to Innovation: Lean-teams, Continuous Delivery, and Artificial Intelligence with MongoDB at Travelers Insurance

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MongoDB World 2019: From Transformation to Innovation: Lean-teams, Continuous Delivery, and Artificial Intelligence with MongoDB at Travelers Insurance