Agile and Efficient Business Outcomes with Artificial ... · PDF filedecision making through...
Transcript of Agile and Efficient Business Outcomes with Artificial ... · PDF filedecision making through...
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Adoption of AI in insurance for enhanced efficiencies and agile decision making
Challenges faced by insurance industry and howAI can help?
For decades, the insurance
industry has worked with
descriptive analysis and risk
assessment. However, with the
availability of big data from
historical processes, IoT and social
media, insurance companies have
the opportunity to take real time
actions based on machine learning
and prescriptive analytics.
Advances in artificial intelligence
(AI) are helping insurers enhance
efficiencies by improving cost
structures and leveraging
automation. The technology has
moved from being a simple rules
based process automating tool to
enabling advanced AI driven
decision making through
underwriting and thinking systems.
Today’s hyper connected
customers expect insurance
services that are at par with those
offered by retail and banking
industries. Changes in digital
technologies and evolving market
dynamics add to the heightened
level of disruption. In addition, as
part of a highly regulated industry,
insurers are required to maintain
humongous amounts of data on
their siloed legacy systems. All of
these factors are compelling
insurers to look for innovative
options in order to deliver high
service levels.
The good news is insurance
companies can leverage AI
solutions and platforms to
address these challenges and
transform into highly efficient
customer centric organizations
(see Figure 1).
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Figure 1: How AI Helps Tackle Industry Challenges
• Virtual assistant uses 3600 customer view, natural language processing and prescriptive analytics to provide personalized customer service
• Increase efficiencies and accuracy through usage of robotics and automation in business processes
• Robots with advance learning capabilities can present a single desktop experience to customers and CSRs
• Machine vision, deep learning and autonomics tech can be combined for scenario analysis and decisioning
• Knowledge virtualization from multiple data sources and IOT data used to provide targeted risk analysis and personalized coverage
AI SolutionsChallenges
Customer’s high benchmarks
of service:
Increasing regulation
& compliance:
Technology advancement
in insurance:
Enterprise architecture
constrained by legacy systems
Complex business processes &
decision making
AI systems are making a perceptible
difference across the insurance
functions. AI can deliver benefits
including:
10-15% improvements in
underwriting
30-35% efficiency in new
business processing and policy
servicing
Claims FNOL process
improvement by approximately
20-25%
Claims assessment
improvement of 10-15%
Approximately 2-4%
improvement in claims payout
with fraud prediction
Wipro HOLMES™ platform based
on cognitive intelligence can
analyze and use the available
humongous data to help insurers
take the customer servicing,
underwriting decision, scenario
analysis, and claims assessment to
the next level.
The Wipro HOLMES™ (Heuristic
and Ontology based Learning
Machine and Experiential System)
platform is a cognitive computing
software. It brings together
comprehensive sets of capabilities
such as language understanding,
vision, learning, prediction,
reasoning, and inferences to
enable businesses to build and
deploy cognitive solutions in the
digital age.
Develop five kinds of AI
applications with Wipro
HOLMESTM:
Digital virtual agents (ngGenie)
Self-learning and predictive
systems
Digital virtual agents (ngGenie)
Visual computing applications
and
Knowledge virtualization and
diagnostics
Some of the key features of
HOLMES™ include:
Enables processing of large
volumes of structured and
unstructured data in different
formats, and analyzes and
understands the content after
they are processed.
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Wipro HOLMES™: A game changing platform for AI driven transformation
Through its smart AI
capabilities, Wipro HOLMES™
can save 25 to 40% of costs
for Fortune 1,000 companies.
Interacts with users and asks
questions through natural
language processing (NLP).
NLP and machine learning are
used as part of chat service to
automate the call center service
operations by answering users’
questions. The output is
machine learning-based and it
continues to learn and
improve the answers over a
period of time by updating
its knowledge
Helps companies
hyper-automate processes,
redefine operations, and
re-imagine customer
journeys. Through its platform
HOLMES Mimictron, the
solution enables complex and
cognitive process automation
that cannot be achieved
through a traditional RPA tool.
Facilitates deployment on
private as well as public
Cloud platforms, addressing
individual business needs for
data confidentiality and
security.
Adapts rapidly to
technological changes due
to its open-source based
design.
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Naturally
Interactive
Knowledge
Representation
Algorithmic
IntelligenceReasoningLearning
Natural Language
to application
services mapping
Knowledge
extraction from
unstructured
Information
Dialogue based
interactions
Semantic mapping
& automated
ontology
construction
Stochastic
computations –
probability,
randomness, fuzzy
logic
Automated
hypothesis
generation and
validation
Applied machine
learning, data and
features based
Common sense
reasoning –
Wordnet, OpenCyc,
Yago
Interactive machine
learning via
observation and
monitoring
Knowledge graph
traversal & deep
inference -
knowledge mining
Natural Language
Generation
Blended Reality
Automated
knowledge model
construction
Knowledge
enhancement
through
annotations
Knowledge
enhancement
through
annotations
Continuous and
adaptive learning
Ontology based
learning
Some of the key attributes of HOLMES™ Artificial Intelligence platform are showcased in Figure 2. A number of function BOTs work in tandem with the virtual assistant (ngGenie) to perform human equivalent actions for the business.
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BOTS are software programs deployed as a service for IT and business processes
Functional BOTS
Building new BOTS
• Classifier BOT
• Summarization BOT
• Do it yourself BOT
• Task Assignment BOT
• Resolver BOT
• Service Ticket BOT
• Curator BOT
• Duplicate Detection BOT
• Diagnosis BOTS
• Text Analytics BOT
• Data Extraction BOT
• Image Processing BOT
• Investigation BOT
• Outlier BOT
• Concept Extractor BOT
• Research BOT
Data Curation
Assemble BOTS
Train & Integrate Algorithms
Deploy BOTS
Verify BOTS
Monitor
Wipro HOLMES™ platform, based
on cognitive intelligence, can
analyze humongous data
volumes to help insurers take
customer servicing, underwriting
decisioning, scenario analysis,
and claims assessment to the next
level. The platform enhances
customer engagement with its
ability to learn from the information
present in policy documents, blogs,
call center logs, underwriter or claim
examiners’ comments, and interact
with the customers using NLP
capabilities. It can provide
personalized customer servicing at
different touch points (chats, calls,
CSR, emails) while helping customers
plan for their future by
recommending the right products,
features, coverage limit, etc. Figure 3
depicts the benefits provided by
HOLMES™ across the insurance life
cycle and Figure 4 provides an
illustration of how HOLMES™ delivers
enhanced customer experience,
underwriter effectiveness and
improved process efficiencies in
the insurance sales process.
How Wipro HOLMES™ helpsthe insurance industry
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Wipro Holmes eKYC (Cross sell & Upsell using Single Customer View)
Figure 3: Advantages of using HOLMES™ across the insurance life cycle
Wipro Holmes eKYC (identify fraudulent customers, content search using ontology)
Wipro Holmes - Robo advice (Product recommendation & UW with social media data
Wipro Holmes (Call center-Service desk automation)
ngGenie (Voice enabled virtual assistant - (Natural Language Assistance, Contextual and Personalized Recommendation)
Wipro Holmes eKYC (Knowledge extraction & fraud identification)
Wipro Holmes (Predictive Analysis & Fraud)
Wipro Sight™ (Drone)
Property / Crop damage assessment and Risk engineering report
Sales
&
Distribution
New Business
&
Underwriting
Policy
Servicing
Claims
&
Payout
BI
&
Reporting
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Figure 4: Illustration of how HOLMESTM can help improve processes
Customer goes to insurer’s website(POS) or calls the call center
Voice enabled virtual assistant
(ngGenie) responds
automatically to call center
calls, or provides the NLP
based chat assistance on the
website using Wipro Holmes.
It asks a few basic questions on customer’s needs and suggests the
right product, policy limits etc. using Knowledge Extractor Bots It
helps customer with the goal based insurance planning
The customer wants to generate quotation
from the insurer website, ngGenie helps
the customer with any questions related
to quotation screens/ fields
During the quotation/policy application stage,
if the policy gets referred to the U/W, it helps the
U/W with eKYC using Knowledge Extractor Bot,
Duplicate Detection Bot & Investigation Bot
Once the policy is issued, the bot generates the covering letter,
policy documents by extracting the right information from required
applications. It sends the document to the dispatch center.
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Policy SalesPoint of Sales Enquiry
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Wipro partners with global
insurance companies to transform
their entire insurance value chain
using the Wipro HOLMES™
platform.
Business challenge:A leading life Insurance company’s
policy validation process was
manually driven by a team of 15 people. This team cross checked
the details provided in the contract
proposal forms and the supporting
documents available in the
documents repository portal
against the information in the
mainframe based policy application
system. Pass, error and detailed
error reports were created
manually. The team received over
4,000 documents each day, which
were manually validated within
three days (on average). The
manual process was prone to a high
degree of inaccuracy, compelling
the client to conduct a separate
audit check on approximately 20% of policies.
Solution:The client leveraged cognitive
intelligence through Wipro’s AI
platform HOLMES™. BOTS
extracted data from the contract
proposal forms and supportive
documents, and verified it against
the system generated details. They
also generated pass, error and
detailed error reports which were
sent to the agents for further
processing. HOLMES™ combined
OCR, image processing, machine
learning and computer vision
technologies to completely
automate this process.
Business benefit: Automating the customer data
validation process with the help of
HOLMES™ led to 80-85% accuracy and improved turn-around time.
Currently, over 4,000 documents
are processed with in a matter of six hours (representing an
efficiency gain of 75%), with just
one or two people assigned to the
task of exception handling.
Wipro HOLMES™: Success stories
Application - proposal forms and supportive documents
are uploaded
Main frame database for policies
Data Extraction-
Bot will extract the data from proposal form.
Data Verification-
Verify extracted data against spool files generated
from system
Agent will confirm for false positive or false negative and
false negative cases will be send to bank for reprocessing
EQC automation for a leading life insurer
Data in the Home Office
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Bot will generate reports –
Pass Report will be transferred to vendor for policy printing
Error report will be given to agent for further processing
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Figure 5: AI Credential - 75% efficiency gain achieved in EQC process for a leading life insurer
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