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WHITE PAPER
EXL XTRAKTO.AITM - PROPRIETARY INTELLIGENT FRAMEWORK TRANSFORMS INSURANCE OPERATIONS
Rahul SinghSenior Consultant, Insurance Industry Solutions
Rohan RegisVice President Insurance, UK & Europe
Written by
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Introduction EXL sees a major opportunity for insurers within the enterprise operational canvas. Whereas insurers have put effort and invested in using analytics to make better decisions and automating manual processing, we notice limited emphasis on the upstream challenge of converting unstructured data to structured data, and extracting usable insights for better decisioning. In other words, only half of the operational canvas is fully serviced.
There are two common roadblocks in automating and adding intelligence to this part of the value chain. The first is that approximately 80% of the data generated from customer inputs is unstructured, and needs to be
converted to a structured, machine-readable form. Second is the considerable challenge of data residing in multiple legacy systems, and a lack of data lakes or single data-assets providing a single source of truth.
Insurance companies today are under unprecedented customer pressure to process real world inputs faster, cost-effectively, and intelligently. Customers expect insurers, like retailers, to remember their choices, use their data effectively and respond to queries quickly. Though many insurers have taken significant steps towards their enhancing operational efficiency, rising consumer expectations and continued cost pressures demand that they do even more. EXL can help.
Insurers today are manually processing increasing amounts of bulk-generated data and documentation on a daily basis, engaging up to 5-10% of enterprise bandwidth, increasing related costs for manual processing, and further impacting the customer experience. In fact, we estimate that a large global insurer can spend around $125 -$175 million and 4-6 million hours annually on manual document
handling across multiple operational processes.
EXL XTRAKTO.AITM - PROPRIETARY INTELLIGENT FRAMEWORK TRANSFORMS INSURANCE OPERATIONS
Exhibit 1: Enterprise operational canvas
Real World InputReal World
Output
Enterprise Operational Canvas
Voice (customer calls, etc.)
Image/Text (customer/partneer communications)
Other Processes
Internal/external data/IoT
Customers
Devices
Process
Convert real-world inputs into structured
data
Organise Enterprise data to make it
usable
Make better decisions Automate processing actions
1. Text to data (insurance certificates, RFPs, etc.)
2. Speech to text conversions to improve compliance and quality within call centers
3. Experimentation with new data inputs to improve enterprise decisions
1. Cloud migrations
2. Master DataManagement
3. Data Privacy, etc.
4. Creation of new self-serviceenvironments andtools
1. Build new business intelligence reports for businesses
2. Enhance existing analytical models with machine learnings
3. Use new data and analytics techniques to improve decisions, e.g., underwriting, fraud etc.
1. Digitize customer journeys
2. Robotics and other automations
3. Use of virtual assistants
Focus of EXL’s Framework
Process analysis and Development of Business Requirements
Project Management
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MOVING AWAY FROM MANUAL:
The case for AI-driven data ingestion
Manual data processing is mundane, time consuming, and error prone. Insurers and brokers worldwide face the unenviable task of efficiently processing large volumes of documents spanning the customer journey, from onboarding to servicing to claims—a deluge of structured, semi-structured, and unstructured data. Processes such as submission, booking and issuance, bordereaux
management, FNOL, and investigation generate complexities due to the large number of documents in a multi-channel input with wide variation. Manually processing these documents takes significant time and effort away from high-value core activities such as underwriting, claims adjudication, and policy onboarding. With customers demanding a near real-time response on every request, insurers and brokers must accelerate processing and swiftly make accurate decisions.
THE CASE FOR NATURAL LANGUAGE PROCESSING IN INSURANCE
Natural language processing (NLP), a field of artificial intelligence (AI), is used to transform manual processes and
analyse large volumes of unstructured data. This technology may be a game changer for insurers. Applied by experts
who understand the insurance domain, an AI-driven data ingestion frameworks can:
•• Reduce error rates
•• Drive better and faster customer experience
•• Improve compliance and reduce monotonous tasks for human operators, while also avoiding the prohibitive cost
of legacy system changes or migrations.
Demand for AI-driven data ingestion frameworks is exploding. The market size is anticipated to grow exponentially
from around $3B in 2016 to around $18B in 2025, clocking 24% annual growth on a year-on-year basis.
Exhibit 2: Documents for manual processing across insurance value chain
EXL Xtrakto.AITM
• Emails• RFP details
• Claim notes• MedicalRecords• Lab Reports
• Policydocuments• Binders
• APS Statements• Plan design booklets
• Census file• Enrollmentdata
• Invoices• Billing supportdocuments
Manual Processing
Illustrative manually processed content within an insurance enterprise
30-50% REDUCTION IN COST AND MANUAL EFFORT
70% IMPROVEMENT IN TURN AROUND TIME
BETTER ACCURACY THAN IN MANUAL PROCESS
NEW DATA FOR DECISION ANALYSIS
Sales Support Underwriting
Onboarding
BillingPolicy
Claims
Complete AI Automationusing AI solution
AI as AssistantJudgement / validation by human
Partial AI AutomationSteps of process eliminated
Digital DisintermediationTransform content at source using digital channels
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EXL’s framework fundamentally changes the way insurance operations are done today, moving from production-based to quality assurance-based ways of working.
Our framework allows insurers and brokers to build a near-touchless data ingestion capability, ultimately unlocking efficiencies and reducing costs. It drives repeatability, scalability and speed to value across the enterprise, reducing effort by up to 70%. In addition, it drives significant auxiliary, growth-enabling benefits, including:
• Enhanced customer experience enabled by lowerturnaround times and improved quality
• Data and process standardisation, as well as new datafor decision analysis
• Insurer ownership
• Better accuracy with reduced error rates
To achieve the levels of efficiency needed to compete in a digital world, insurers and brokers must not only think smarter, but also execute smarter, by embedding intelligence into every business process.
Introducing EXL Xtrakto.AITM -
Proprietary Intelligent Framework:
Move up the curve with AI-powered automated data
EXL Xtrakto.AITM is a next-generation, AI-based framework which uses AI and NLP to bring significant efficiencies to historically manual processes such as extraction and document classification. Our framework provides insurers with intelligent data ingestion capability that understands what documents are about and the information they contain, extracts relevant information and sends it to the right place.
Exhibit 3: Manual data ingestion in broker submissions
Consider an example of a broker submissions process for a large insurer. As Exhibit 3 denotes, multiple pain points arise from non-standardised formats, missing information, manual application checks, duplication efforts, and no lead generation insights for increased profitability.
Submissions and Quotes – Current State
Paper
Portal
Read documents and Scan for content
Manually search for required content
Manually key content into application
Business Application
Submissions from Brokers
Review application & documents for duplicate, new/existing Insured name, Market, Segment, Product, Missing information etc.
Extract data from various sources such as loss runs, exposure statements, sanctions etc.
Key information in multiple applications to prepare and summarise risk assessment
Risk accepted or decline decision communicated to Broker/Sales and send rating instructions
Manual ActivityInefficient process with limited standardisation
across LOBs,
Manual checks and minimum fields not
established
Manual data extraction and lack of insights for
lead prioritisation
Duplicated effort of manual rekeying of data
into multiple systems
Challenges and Opportunities
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It incorporates some of the latest NLP algorithms, and customises them further by exposing them to real-world data and tasks. For any new NLP use case, our framework consists of various customised pre-trained models which accelerate the solution development process. Each use case will have a varying development timeline and estimated benefit, depending on nuances such as data availability, languages involved, handwriting utilisation, and other areas.
EXL Xtrakto.AITM is where three key frameworks levers meet to move beyond just automation and deliver the
right technology solution for enterprises at scale:
• Lever One - Operating Model: Triage incoming workby complexity and client into tiers; move the work tothe right location
• Lever Two - AI-Driven Extraction: Automaticallyextract and classify data using combination oftechnologies, such as OCR, traditional machinelearning, and deep learning
• Lever Three - Operationalising the frameworks:End-to-end process re-engineering; embed AI-modelsinto the operational workflow and drive changemanagement within teams
These levers, when applied across the current state of document processing, lead to an evolution of the current heavy human, or highly manual, state, to an NLP and AI-enhanced thin human, or highly automated, state. Thin human models are digitised and machine-dependent, with extremely limited need for human intervention.
Consider the previous example of broker submissions process. End-to-end transformation to the target state is possible when the above three levers are applied at various levels of process: (See Exhibit 4)
TRACKING ADVANCES IN NLP TECHNIQUES
NLP is rapidly evolving, with new breakthroughs
identified almost every week. Powerful machine
learning algorithms incorporating concepts such
as transfer learning are being developed using
pre-training to build on existing algorithms.
In order to evaluate these NLP models, the
industry-wide benchmark General Language
Understanding Evaluation (GLUE) has been
established. It is based on a set of complex
NLP tasks such as text classification, question
answer pairing, and other capabilities. Within
one year, more than ten NLP algorithms have
been developed which performed better than
humans on all tasks.
Does this mean we will be able to replace
humans in various document processing
processes? No. While we are making rapid
progress on the AI/NLP front, we are still far
from general AI algorithms which can directly
replace humans in certain tasks. Even the most
powerful NLP algorithms developed to date
are an example of narrow AI, which only works
for a specific set of tasks and an on the data
which it has already been exposed to. Hence,
the most advanced NLP solutions cannot work
as plug-and-play software —instead, they
contain several modules which must be stitched
together or custom-developed, as needed.
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approach it, moving beyond considering, conceptualising and piloting towards deployment at scale. EXL’s two-pronged approach helps clients set up a factory model to support swift delivery.
1. Prioritisation Roadmap
EXL’s market-tested segmentation approach for manually processed content builds on four broad categories based on content characteristics and feasibility of transformation: (See Exhibit 5)
Our framework also comprises several modules which promote faster deployment and scaling of the framework. Implementing an intelligent automation solution with AI at scale will re-invent the data management cycle, drive human-machine collaboration, and achieve at least a four-fold return on investment in the technology.
OPERATIONALISING EXL Xtrakto.AITM:
Building towards an intelligent insurer
The key difference between insurers investigating and industrialising intelligent frameworks lies in how they
Exhibit 4: Transformed target state for broker submissions
Business Application
Excel
Automated Solution
Target State
Document A
NLP MachineLearning
Document Generation
Increased adoption of online channels for standardisation
Document classification, automated data extraction, checks and data entry
QA of automated output (outsourced, centralised)
Levers and Framework to Scale the Capability across the Enterprise:
Operating Model• Documents triaged by level of
complexity & client priority• Most QA can be moved to offshore
Process Transformation• E2E process decomposed into
component parts• Seamless click through process to QA documents before “releasing”
EXL Xtrakto.AITM
• Automation of data extraction• FTE activity shifts from reading and keying to validation & QA
A
A
B
B
C
C
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2. Execution roadmapThe use of intelligent automation at scale remains relatively immature for many insurers and brokers, inhibiting complete transformation. Some apply transformation levers in a siloed manner, limiting progress beyond pilot phases. EXL’s market-tested, three-phase approach deploys the automated data ingestion framework on the above-mentioned content categories, with different blends of human and NLP/AI technologies:
Opportunities are then prioritised for framework implementation based on two filters:
• Business value: The nature of work (classification,extraction, search), high-level process flow, costof error, average handling time for manual effort,volumes
• Execution complexity: Availability of AI trainingdata, document types, variety of document formats,handwritten docs, language, current workflow, ready-to-use algorithm availability
Exhibit 6: Operationalisation roadmap for content categories
Exhibit 7: Implementation timeline for a sample “On deck” category
Exhibit 5: Prioritisation roadmap for content categories
Content Category Content Characteristics Illustrations Time to Transformed State (approx.)
Next-gen ready• Structured data forms• Certain semi-structured forms
• ACORD forms• Financial statements• Emails for classification and routing 30 – 90 days
On deck
• Semi-structured forms with moderate/high volumes• Unstructured documents with high volume• Communication generation(template-based)
• Loss runs, carrier quotes• Invoices, collections• RFP, plan documents 180 – 270 days
Complex but possible
• Language-heavy document generation• Lower accuracy models
• RFP, proposal creation• Extraction modules from
unstructured docs Up to 700 days
Uniques
• Information collection from customers• Communication generation (non-template based)
• Email communication (non-standard responses) NA
EXEC
UTI
ON
CO
MPL
EXIT
Y D. “UNIQES”
Deprioritise
C. “COMPLEX BUT POSSIBLE”
Longer term implementation
B. “ON DECK”
Prep for second phase implementation
A. “NEXTGEN READY”
Prioritise for short term implementation
PHASE I
PHASE II
PHASE III
NEXTGEN: NLP/AI + “THIN HUMAN” Solution
“ON DECK”: Offshore/Onshore Human with Digital/Al prepping for NextGen
“COMPLEX BUT POSSIBLE & UNIQUES”: “HEAVE HUMAN + THIN DIGITAL”
Operating Model
(via arbitrage)
Process Transformation
Automated Framework
NA Immediate Short Term
Immediate On-going Medium Term
Immediate Medium Term Long Term
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Continuing with our example of automated data ingestion for the certificate of insurance (COI) process, in order to support swift delivery of capability at scale, the three levers could be applied over a period of six to eight months. However, value in terms of utilising employees more efficiently for lower costs can be realised in as little as two months. EXL follows a phased approach that
Exhibit 8: Typical approach for operationalisation
CASE STUDY:
How a large global insurer expedited value from EXL Xtrakto.AITM
EXL can point to multiple use cases across the value chain where insurance and financial services clients have benefited
from our framework. A global insurer currently implementing EXL’s AI based data-ingestion framework partnered with EXL to
reimagine the CoI process workflow. The three levers are being applied in a phased approach over a six-to-eight month period,
and will enable the client to realise cost savings in a short space of time to intelligently automate almost half a million requests
per year from more than 8,000 clients.
EXL’s framework is enabling the extraction of meaningful insights from documents, emails and customer service data. These
insights allow quicker delivery of accurate customer outcomes, improvement in operational resilience, and increases capacity
for the client’s operations teams to focus on more value-adding tasks.
As a result, the client will see a >50% improvement in efficiency.
continuously refines the benefits case as we build out NLP-driven models on a much larger data sample and executes an implementation and workflow integration plan over the timeline. The execution timeline contracts with each use case, as the capability matures. Once set up, multiple use cases can be executed in parallel.
EVALUATE BUILD SCALE DEPLOY RAMPUP
• Process evaluation with samples, required outputs, etc.
• Categorize opportunities– Complete AI Solution– AI as Assistant– Partial Automation– Unique
1-2 weeks 2-3 months 3-4 months 1-2 months Ongoing
• Design and scope model requirements
• Build initial models with sample data
• Develop initial benefit estimate
• Scale up initial models with larger data
• Build model pipelines• Finalize benefit and
commercials
• Integrate solution with existing workflow
• Rollout NLP/AI solution• Monitor various
operational and quality metrics
• Ramp-up production solution
• Ensure continuous improvement
• Deliver additional cost improvements
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• Domain expertise that allows us to underwritebenefits: NLP framework development leverages ourvast domain expertise. Most importantly, we ensurethat our framework output plugs into the operationalworkflow that allows human representatives tomanage exceptions and allows EXL to underwrite thebenefits
• Advanced NLP capabilities: Our proprietary stackof accelerators and frameworks built by our largeteam of data science professionals are fine-tuned forspecific use cases; our stack speeds up the AI modeldevelopment and integration process by up to 40%
• Customisation: We understand that every insurerand broker has different processes, and do not follow a ‘one size fits all’ approach. EXL caters to multiple document types, including structured, semi-structured, and unstructured, and formats
including jpeg, tiff, emails, and html
• Flexible deployment with modular components:We can deploy our framework on premise or onthe cloud, and allow our clients to choose solutioncomponents. For example, many clients chooseto take our user interface and validation screens,while others have requested that we build customerdocument portals on their website
If you’d like to explore how leading insurers and brokers are leveraging our solution and the value it could deliver for you, we’d welcome the opportunity to talk and arrange a demo for you.
Acknowledgements/Contributors:
Raghav Jaggi Mohit Manchanda Roopak Chadha Wayne Reed
What Differentiates EXL’s Proposition
EXL’s hybrid framework is differentiated from available market solutions due to its AI/ML driven engine and ability to generate domain specific insights. It addresses all critical dimensions pertaining to solution categories in the market –variety in input documents, OCR capability, NLP/NLU capabilities, domain expertise, ability to customise extraction engine, speed of deployment, and human + digital integration.
Why EXL?
EXL’s insurance domain expertise and digital capabilities are globally recognised. We were named as The Leader in the Everest Group’s 2020 P&C Insurance PEAK™ Matrix. EXL demonstrates significant breadth of proprietary AI/NLP models, frameworks, and toolkits. Our solution brings enhanced NLP, machine learning, and artificial intelligence capabilities for accelerating document processes to meet business objectives for our clients. We believe that our solution is differentiated in four key dimensions:
Exhibit 9: Intelligent Framework-Market Landscape
A
OCR Vendors > Software to convert
scans to machine readable text
B
C
NLP Platforms > NLP/AI platforms with
pre-built solutions/ services for generic problems
Niche Solutions > Point solutions for
industry specific problems
EXL Xtrakto.AITM
To see what Insurance clients are saying about EXL’s AI and NLP capabilities, please visit this link: https://www.exlservice.com/AIatScale
Gaurav Iyer Sumit Taneja Chaithanya Manda
EXL (NASDAQ: EXLS) is a leading operations management and analytics company that helps our clients build and grow sustainable businesses. By orchestrating our domain expertise, data, analytics and digital technology, we look deeper to design and manage agile, customer-centric operating models to improve global operations, drive profitability, enhance customer satisfaction, increase data-driven insights, and manage risk and compliance. Headquartered in New York, EXL has more than 31,000 professionals in locations throughout the United States, the UK, Europe, India, the Philippines, Colombia, Australia and South Africa. EXL serves multiple industries including insurance, healthcare, banking and financial services, utilities, travel, transportation and logistics, media and retail, among others.
For more information, visit www.exlservice.com.
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