Insurance Data Fabric - IASA 2.4... · Business case defines the value with supporting use cases. Data fabric is the flypaper that collects data (IOT – sensors and wearables, chatbots,

InsuranceData Fabric

David MoorheadEY Senior Manger

November 2019

Page 2Confidential — all rights reserved © Ernst & Young LLP 2019

Insurance Data Fabric enables saleable next generation architecture

Illustrate how architectures continues to evolve to simplify data and fuel innovation usingartificial intelligence and data fabric technologies on top of existing technologyinvestments to ease ‘data friction’.

Today’s session2

Business demand for information and innovation constant. Technology trends realized.With Big Data solutions becoming commodities. Bold prediction – data and reportanalysts are disappearing

Last year’s session1


DIVIDER

3

1. Market Leading Platform

1. Industry trends around data


Insurers face significant pressures to grow profitable business, loweroperational risk and differentiate their brand and service.

Focus on building a stable, efficient core systems foundation and data management. An unstablefoundation will compromise product and servicing differentiation, as well as, increase complianceand solvency risk.

Customer engagement and experience is where differentiation happens. Insurers are focusedon creating competitive products and a rich customer experience platform that caters to thechanging needs of a dynamic and a more price sensitive customer spanning both personaland commercial lines.

Data and analytics are required to adopt to continuous evolving demands, not a one-time event orneed. Insurers need to design data solutions that are flexible, extensible and accurate. Innovationand digital demands required analytics that adapt to evolving market conditions and deliver valueiteratively, allowing you to incrementally introduce disruptive technologies, to test, learn and adapt.

4

Data and Analytics

Differentiation –Digital and Innovation

Operations –functionality,

compliance andefficiency

1. Industry trendsaround data


Expectations around data access, accuracy, security and management. Continuewith commoditization of Artificial Intelligence and Big Data technologies.


Futuristic user experience: Providesweb- or mobile-based interfaces, andenable opportunities for InsuranceCarriers to improve data usage, fasterinsights, optimize experience, lowercost and enhance operational efficiency

Democratize data: AI powered businessintelligence platform enables everyone across

enterprise to ask questions in naturallanguage and receive answers based on data

Familiar usage patterns: Insurance industryhas adapted various artificial intelligence andnatural language processing solutions usingIntelligent virtual assistants (Alexa etc.)

Harvesting existing platforms: Level ofsophistication is driven by integration withexisting systems, as well as the quality and

quantity of data sources available

Traditional data analysis: Provides dataanalysis capabilities which enable users to

discover insights across multiple datasources in the enterprise with full history

i.e., Facebook like data capture

Limitless velocity, variety and volume:Direct access to social media, wearablesand other IoT sources that readilyoverlays with my internal applications.


Leading insurers have met the new customer demands with ambitious visions onhow to use data as a strategic asset.

Regulation andCompliance

Seeking to monitorand manage risk asregulations evolve

Innovation and Digital

Ease of use at a lowerprice point

Core SystemTransformation

More efficient processingwith better results

Predictive andImbedded AnalyticsFinal step to action

with tangibleresults

New technologies at a lowerCost of Ownership

Ease of use and efficiency,at a lower price point

ü

3

Data has many masters and always a new set of tools with higher expectations


Latest technology promises for the “Facebook of Data”

Data fabric enables “frictionless access” and sharing of data in a distributed dataenvironment. It enables a single and consistent data management framework, which allowsseamless data access and processing by design across otherwise siloed storage. - Gartner


DIVIDER

7

1. Market Leading Platform

2. Architected innovation


Business case defines the value with supporting use cases. Data fabric is the flypaper that collects data (IOT –sensors and wearables, chatbots, mobile drones, etc.)

Architecture begins with the end already in mind and how we usetechnology. Data fabric is the cost-effective enabler.

1.Simple motor claims using IoTand Chatbots

► In simple motor claims, the notification of the claim can bemade by the car itself through IoT sensors. Use ofchatbots means there is no human involvement fromthe insurer in the full FNOL process

► As the crash occurs, the IOT sensors alert the insurerand a claim is automatically created in ClaimCenter

► A chatbot starts a text conversation with thecustomer to check they are okay, collect moreinformation about the claim and initiate supplierservices

2.Auto adjudicate simple property claimsusing Drones and Chatbots

► In simple property claims, the notification and inspection ofclaims can be managed through chatbots and drones,without the need for any human intervention

► The customer initiates the claim through their mobileapp which launches a chatbot conversation to collect allof the necessary information, and schedule the droneinspection

► A drone inspection is deployed, live streaming images,which are analysed by AI, and repair services areautomatically notified to fix the issue

3.Marine claims using Blockchain► In Marine claims, IoT automatically triggers a claim

notification through sensors on the ships, which arereceived through cloud infrastructure and linked to apolicy contract on blockchain

► Claim notifications are sent from the blockchain toClaimCenter automatically to trigger an FNOL claim. Asingle version of truth is written on blockchain withdetails of the events which are shared across allparties in real time

► The blockchain is used to manage claims across theclaim lifecycle, instantly updating claim operations

Chatbots

2. Architectedinnovation

Data fabric – Common Data Services – collects and provide ‘frictionless experience’


2. Auto adjudicate simple property claims using Drones and ChatbotsUse case example – requires Next Generation Architecture to provide saleable and effectivedata solutions that genuinely removes ‘data friction’.

Architecture begins with the end already in mind and guides how we usetechnology. Data fabric is the cost-effective enabler.

Chatbots


Voice or textconversationthrough web ormobile channel

Machine learningbased queryparsing throughNLP and phoneticmodule

Intentclassification &Context (entity)recognition

AI smart querybuilder - metadatadriven framework fordynamic querygeneration

Query executionagainst data fabricvia common dataservices

Response in text,voice or chartformats

Next Generation Solution Architecture

Page 10

Demonstration Video


What does that demo look from technology view?


Processing stages – What is my loss ratio?

AI-SmartServiceEngine

MTD Loss Ratio forApril “Loss Ratio”

Queryparsing

IntentMapping

Contextvariables

Acct date <= ‘201904’

Metric : “”Loss Ratio”

Loss ratio for April2019 MTD is 72%

YTD Loss Ratio forPersonal Auto by

Agent

“Personal Auto”

grouping: “Agent”

Line of business=‘Personal Auto’

SELECT D02.AGNT_NAME,cast(isnull(SUM(ISNULL(F01.YTD_IND_PD_AMT +F01.YTD_MED_PD_AMT + F01.YTD_ALAE_PD_AMT +F01.YTD_ULAE_PD_AMT +F01.YTD_IND_RSRV_CHNG_AMT +F01.YTD_MED_RSRV_CHNG_AMT +F01.YTD_ALAE_RSRV_CHNG_AMT +F01.YTD_ULAE_RSRV_CHNG_AMT,0) )/nullif(sum(F01.YTD_EARNED_PREM_AMT),0)*100,0)as decimal(18,2)) AS LOSS_RATIOFROMV_FACT_PREM_CAL_YR F01INNER JOIN DIM_AGENT D02 ON F01.AGNT_ID =D02.AGNT_IDINNER JOIN DIM_PRODUCT D01 ON F01.PRODUCT_ID= D01.PRODUCT_IDINNER JOIN DIM_MONTH D05 ON F01.ACCTG_PRD_ID= D05.MTH_IDWHERELOB_CD in(‘PersonalAuto')AND D05.MTH_ID = '201904'group by D02.AGNT_NAME

“by Agent”

Time period: “YTD”

“YTD”

Loss Ratio for April2019 YTD is 76%

Data Service


DIVIDER

12

3. Delivery Certainty

3. Artificial Intelligence and Data Fabric


Data Fabric is essentially ‘data micro services’ that make pulling disparatedata simple.

3. AI and Data Fabric

FIT-FOR-PURPOSE STORAGEof data on-premise or on public/privatecloud(s) that use disparate storagestructures such as object and file stores(Hadoop, Cloud, NoSQL and containers)

ECONOMIC ALLOCATION OFCOMPUTATIONacross ephemeral or long-running clustersand containers

§ Efficient utilization of infrastructureacross disparate environments

§ Retire Legacy Systems while businesssystems run continuously withoutdowntimes

§ Intelligent Data Governance &Management

§ Automation of data ingestion,movement and persistence acrossenvironments

Enabling Data Capabilities

§ AI Driven Data Discovery &Orchestration

§ Consolidated Real Time View ofdata across on-prem and multi-cloud environments

EFFICIENT DATA USAGEthrough virtualization of infrastructure,storage and computation


Data Fabric key features are aligned to the existing data managementcommitments.

3. AI and Data Fabric

§ Uses AI/ML based logic for intelligent decision making (e.g. by predicting cluster failoversand adjusting resource allocations, automatically distributing compute workloads basedon ongoing jobs, etc.)

§ Security policies defined, administered and enforced centrally§ Fine grain access control (RBAC and ABAC) enforced across enterprise

§ Shared metadata across enterprise embedded within the fabric§ Governance of enterprise wide data

§ Graph Based interconnected APIs lend to optimized network loads and datausage

§ Over and under fetching of data is mitigated allowing for better digitalexperience

§ Need based, standing up and down clusters to simulate, validate deep learning models§ Cloud test resources are allocated based on demand and availability§ DevOps automation in promotion of applications from dev to test to production using

available infrastructure

§ The fabric provides AI driven methods to apply privacy as needed based on userrequests for data

§ Privacy methods are consistently applied across data stores, computational methodsand data usage

Data Fabric(Data Micro Services)

EMBEDDEDINTELLIGENCE

SECURITY

PRIVACY

GOVERNANCE& METADATA

APIS DATAACESS

SIMULATION &VALIDATION


Insurance Data Fabric enables saleable next generation architecture

15

Technology without purpose has limited or no value. Use cases align the business case to incremental delivery using an enterprise,next generation architecture to lower total cost of ownership, lower delivery risk and enable proper data management. Withoutusage or management, all architectures become a ‘swamp’ or ‘friction-filled” experience.

Architect vis Use Cases2

Technology without purpose tends to be under utilized. Insurers have invested in ‘Big Data’ with mixed results. Best results arebusiness driven with clear set of capabilities to focus next generation technologies and measure benefits to justify the investment.

Business Case Alignment1

Innovation, digital, and core system transformation all require accurate and timely data for success. Data transformation are brokeninto smaller, properly sequenced or road-mapped deliveries. Each incremental build out uses the enterprise architecture promotingproper next generation tools,, as well as, re-use of data that lowers a transformation’s delivery risk and speed to market.

Architected Innovation3

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Insurance Data Fabric - IASA 2.4... · Business case defines the value with supporting use cases. Data fabric is the flypaper that collects data (IOT – sensors and wearables, chatbots,