How to Become an Analytics Ready Insurer - with Informatica and Hortonworks

How to Become an Analytics Ready Insurer Wednesday, March 25, 2015

Featured Presenters

2

Cindy Maike General Manager, Insurance

Hortonworks

Josh Lee Director, Global Industry Marketing

Informatica

Page 3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved

How to Become and Analytics-Ready Insurer

Cindy Maike, General Manager - Insurance


Data never sleeps and…

Source: www.domo.com

“with every status we share, every article we read or every photo we upload

we are creating a digital trail that tells a story…”


Clickstream Capture and analyze website visitors’ data trails and optimize your website

Sensors Discover patterns in data streaming automatically from remote sensors and machines

Server Logs Research logs to diagnose process failures and prevent security breaches

New types of data

Sentiment Understand how your customers feel about your brand and products – right now

Geographic Analyze location-based data to manage operations where they occur

Unstructured Understand patterns in files across millions of web pages, emails, and documents

It is growing exponentially – with new business challenges and potential delays


Hadoop driver: Enabling the data lake SC

ALE

SCOPE

Data Lake Definition •  Centralized Architecture

Multiple applications on a shared data set with consistent levels of service

•  Any App, Any Data Multiple applications accessing all data affording new insights and opportunities.

•  Unlocks ‘Systems of Insight’ Advanced algorithms and applications used to derive new value and optimize existing value.

Drivers: 1.  Cost Optimization 2.  Advanced Analytic Apps

Goal: •  Centralized

Architecture •  Data-driven

Business

DATA LAKE

Journey to the Data Lake with Hadoop

Systems of Insight


Business and application silos have caused many insurance carriers to have disparate data and processes; however, a Data Lake enables “all data is searchable” capability

Product Pricing & Underwriting

•  Risk/claims experience analysis

•  Profit testing •  Reinsurance analysis

Risk Analysis •  Identification of risk factors •  Analysis and development

of risk models •  Trend analysis

Campaign •  Target selection •  Response optimization •  Communication tracking •  Campaign performance

Profitability Analysis •  Margins vs. claims and expense

analysis •  Product, segment, customer

profitability

Claims Analysis •  Analysis of claims types,

frequencies, amounts claimed and paid

•  Fraudulent claims analysis •  Analysis of settlement period

Product Management •  Product performance analysis •  Analysis of competitive products •  Market requirements analysis for new

products •  Forward premium earning analysis

Segmentation Management

•  Segment definition •  Segment activity analysis •  Segment targets/

achievements analysis •  Cross-selling analysis

DATA LAKE

Systems of Insight

Customer Management and Retention

•  Response optimization •  Communication tracking •  Campaign performance •  Target selection •  Persistency experience analysis •  Identification of potential lapses •  Competitive analysis


APPLICATIONS

Analytics-ready Insurers leverage a modern-day data reference architecture blending traditional and new data

HDP Hadoop Cluster

1

Mul1tenant Processing: YARN (Hadoop Opera,ng System)

NoSQL

HBase Accumulo

Stream

Storm

Others

Security (Ranger, Knox, Hive, HDFS, etc...)

Governance Falcon

Script

Pig

SQL

Hive

Java

Cascading

Search

Solr

° ° ° ° ° ° ° ° ° ° °

° ° ° ° ° ° ° ° ° ° ° °

° ° ° ° ° ° ° ° ° ° °

Opera1ons (Ambari, ZooKeeper, Oozie)

Ingest

Sqoop Flume KaJa

DATA REPOS

DATA SOURCES

•  Enhance agent productivity •  Detect fraud •  Launch prevention services •  Price using sensor data •  Subrogation & litigation •  Claims leakage & accuracy •  Mileage verification •  Payment analytics

Transcriptions

EDW

Underwriting

Claims

Commission& Billing

Product

Finance

Application Documents

Emails CRM Records

Medical Bills Underwriting Notes

Prior Loss & Vendor Repts

Policy Records

Marketing Research Claims Data

Sales Portals Mobile Telematics

Mortality Tables

Clickstream Web Logs

Risk & Claim Models Social Media

In-‐Memory

Spark

Metadata Management HCatalog

° °

° °

° N °

Linear Scale Compute & HDFS Storage


Customers using HDP to meet insurance priorities

Rising Claims Costs (frequency and severity)

Top 3 Challenges facing Insurance Industry Today

Key Business Requirements

Change in Customer Engagement Model

Data Explosion (Complexity of Risk/

Underwriting Information)

n  Access to internal and external disparate risk information to make informed underwriting and risk analysis decision (support an underwriting ‘cockpit’ for complex risks)

n  Sensor-based insights for pricing (driving habits and usage) n  Identification of similar risk characteristics and performance (explore and analyze prior

submissions), text analytics of unstructured submissions n  Ability to “tailor personalized prevention based services” vs. traditional product-based

policy offering n  Support social/insured ‘supply chain’ network analysis

n  Single customer “golden” record n  Enable differentiated experience, guidance and recommendations based upon customer

insights n  Call center/agent productivity and effectiveness gains through “total view of the

customer” n  Support cross-sell opportunities n  Web path optimization (identity, state, insight, guide, action)

n  Sensor-based insights for claims analytics: product liability, claimant liability (e.g. auto claim: speed at time of accident, proximity, application of brakes)

n  Analysis of unstructured claim notes (text analytics) and information n  Link analysis across multiple claims n  Support enhanced subrogation opportunities n  Claim severity model and enhanced loss prevention models


Insurance Business Value Matrix using HDP N

ew T

ypes

of

Ana

lytic

s

New Types of Data

New Types of Data New Analytics Apps

•  Sentiment

•  Click-stream

•  Sensor

•  Geographic

•  Server Logs

•  Unstructured

Existing Data

Exis

ting

Ana

lytic

s

RDBMS

MPP

EDW

•  EDW & ETL data & load balancing

•  Cost & flexibility •  Building new skill

sets •  Scale out using

commodity hardware

•  Single-View of Customer showing full 360-degree profile and history

•  Clickstream analysis for Next Best Action with Customers

•  Analyzing submission and claims models against larger historical data sets

HDP

HDP

New Historical View

IT Optimization New Data Influencers

•  Collecting Sensor/Telematics for Usage Based Insurance

•  Sentiment •  Enhanced Loss

Control / Prevention Services

•  Needs based coverage vs. traditional coverage

HDP

New Analytics Applications •  Text Analytics and Link

Analysis for Claim Anomaly and Fraud Analysis/Detection

•  Enhance Risk Analysis with Related Party Network Link Analysis

•  Enhanced Claim Severity and Frequency Models using “new” predictive data

HDP


Hadoop for the Enterprise: Implement a Modern Data Architecture with HDP

Customer Momentum

330+ customers (as of Q4 2014)

Hortonworks Data Platform •  Completely open multi-tenant platform for any

app & any data. •  A centralized architecture of consistent

enterprise services for resource management, security, operations, and governance.

Partner for Customer Success •  Open source community leadership focus on

enterprise needs •  Unrivaled world class support

•  Founded in 2011 •  Original 24 architects, developers,

operators of Hadoop from Yahoo! •  600+ Employees •  800+ Ecosystem Partners


HDP: Any Data, Any Application, Anywhere

Any Application •  Deep integration with ecosystem

partners to extend existing investments and skills

•  Broadest set of applications through the stable of YARN-Ready applications

Any Data Deploy applications fueled by clickstream, sensor, social, mobile, geo-location, server log, and other new paradigm datasets with existing legacy datasets.

Anywhere Implement HDP naturally across the complete range of deployment options

Clickstream Web & Social

Geoloca1on Internet of Things

Server Logs

Files, emails

ERP CRM SCM

hybrid

commodity appliance cloud

Over 70 Hortonworks Certified YARN Apps


Why Informatica + Hortonworks Features Benefits Visual development environment

Increase productivity 5x, operational efficiency, reuse

200+ high-performance connectors (legacy & new)

Move all types of customer data into HDP faster

100+ pre-built transforms for ETL & data quality

Provide broadest out-of-box transformations on Hortonworks

100+ pre-built parsers for complex data formats

Analyze and integrate all types of data faster

Joint MDA reference architecture

Complementary capabilities to accelerate customer success

100K+ trained Informatica developers

Use existing & readily available skills for big data


Call to Action

•  EXPLORE à Learn from your current + New Data •  Look outside your industry for additional

leaders

•  DISCOVER à New insights with traditional data and “new data” sources”

•  Avoid using traditional analysis methods with new data…it is truly different…... •  Understand the skills required •  Leverage the “right” technology options

•  INNOVATE à Launch new innovative products, better serve and attract customers

•  Don’t fear “experimentation” •  Capitalize on the Opportunity


Closing thoughts

“Some people see a trend & see threat. Innovators see the same trend & see opportunity.”

Jim Carroll, the world’s leading futurist, trends & innovation expert

Become an Analytics Ready Insurer Josh Lee – Global Director, Insurance Marketing March 2015

Who is Informatica?

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•  900+ in Financial Services including:

•  25 out of the top 30 P&C, Life Providers in the U.S.

•  35 out of the top 50 global banks

•  85 Insurance Customers

•  Leader in #1 growth sector within infrastructure software: Data Integration and Data Quality

•  In the Leaders Quadrant on virtually every Gartner Magic Quadrant

•  Data Quality

•  Data Integration

•  Master Data Management

•  Data Security

Why Do Analytics in Insurance?

•  Grow Revenue •  Average insurers must add 13% new policyholders annually

to maintain flat revenue •  Combat Fraud

•  Insurance fraud tops $40B/year •  Fraud drives up premiums by up to $700/year per family

•  Develop New Products •  By 2030 the “over 65” demographic will be 20% of population

•  Reduce Risk •  Since 2013, over 50% growth in magnitude 3+ earthquakes

in Oklahoma •  …and a whole lot more!

18

Analytics…Good and Bad

19

Unknown – 87% Bob – 6%

Ann – 7%

How does this happen?

When I wanted this!!

Courtesy of Beautiful Data

Where Does My Data Come From?

20

Apps

Files

Custom

Structured

Structured but needs to be integrated

Structure varies or absent, integration

required

Structure indeterminate

Integration Required, formats and storage

constrained

Making the Most of Infrastructure

21

Traditional Data Warehouse

Documents and Emails Social Media, Web Logs

Risk Analytics Claims

Data Quality

Powercenter Big Data Edition


Mainframes, Apps

•  The Reality is… •  Everyone has LOTS

of data sources •  New and Old data will

be around forever •  But…

•  New data types and shapes every day

•  Data management needs to be reusable

•  Data volume is growing

Benefits to Insurers

22

Risk

Return

Current

Desired?

Portfolio Optimization

More Storms, More Data

Bigger, More Diverse Populations Data From Everywhere

A Powerful Analytics Foundation

23


Integrate, clean and optimize

Discover, shape and leverage

Thank You And

Questions!!

24

Next Steps FREE TRIAL DOWNLOADS

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How to Become an Analytics Ready Insurer - with Informatica and Hortonworks