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Transforming Insurance Operationsthrough Data and Analytics

Roger Oldham

www.datalytyx.com

A day in the life of ……..

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About Speaker: Roger Oldham

• Founder and Managing Director of London Market Technology Exchange; London Market People.

• 28 years in the London Insurance Market. Industry change and modernisation specialist.

• Ex-Head of Claims and Head of Market Practice positions in Aon, HSBC Insurance Brokers and Marsh.

• Qualified Mediator of the Chartered Institute of Arbitrators in London and Fellow of the Chartered Insurance Institute.

• Non-Executive Advisor to Datalytyx

Roger Oldham

BA(Hons) FCII MCIArb FInstLM

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Objectives Today

1. Analytics in InsuranceState of market Readiness

Uses per sector of insurance

2. Compliance and RegulationChallenges

3. Other challenges re data “Accidental Architecture”

Data access challenges

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My sector definitions for InsuranceP

erso

nal /

Ret

ail

• High volume, low value

• Life, Health, Home, etc.

• Composite providers

Cor

pora

te /

Who

lesa

le • Low volume, high value

• Marine, aviation, property, casualty

• Insurance binders

Rei

nsur

ance

• Risk transfer and arbitrage

• Treaty reinsurance

• Facultative Reinsurance

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Insurance Market State of Readiness for Big Data & Analytics

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86% of insurance CEOs believe

technological advances will transform their

businesses in the next 3 to 5 years – more

than any other factor.

Unleashing the Value of Advanced Analytics in Insurance

– McKinsey (2013)

“Innovations in analytics modelling will also enable

carriers to underwrite many other emerging

risks that are underinsured including

cybersecurity and industry-wide business interruption

stemming from natural disasters.”

Lots of commentators……..

“Big data will undoubtedly be the

thing that will reshape our industry.”

17th Annual Global CEO Survey - Key Findings in the Insurance

IndustryPWC

February, 2014

Can Reinsurers Ignore Big Data? – Bryan Joseph, Partner

and Global Actuarial Leader, PWC, March,

2014

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Global Data Analytics Survey (for Insurance), 2014, PWC

Infographic - Global Data & Analytics Survey 2014 (for Insurance) PriceWaterhouseCoopers, September 2014

Executives

71% yes - have changed the way they approach decision making as a result of data analytics

23% no , but plan to

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Uses

Interesting

No mention of analytics for compliance & regulation

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In summary …….. its multi-speed!

ReinsuranceCorporate / WholesalePersonal/Retail

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Analytics distinctly different per insurance sector…..P

erso

nal /

Ret

ail

• Fraud analytics and detection

• Behavioural analytics

• Geospatial analytics: property, weather, etc.

• Affinity groupsC

orpo

rate

/ W

hole

sale • Geospatial analytics:

transit routes, flight paths

• Political and territorial risk predictive analytics

• Binders Analytics: per binder contract, per peril, etc.

Rei

nsur

ance

• Complex analytics e.g. probability distributions

• Catastrophe modelling based on actuarial analytics

• Distribution of outcome and contract pricing

Compliance & Regulatory Analytics re: claims handling, complaints handling, solvency, liquidity, treat customers fairly, etc.

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Consider the challenges of compliance and regulation

What are the specific challenges around:

• Compliance

• Regulations

• Performance optimisation

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Regulation increasing………and it now has teeth!!!

Regulators: FCA / PRA• Liquidity & Capital Adequacy• Solvency II• Transparency Framework• Claims Handling• Complaint Handling• Treating customer fairly• Changes in global risk profiles

Fines• Swinton Group - £7.38m• Besso - £315k• Stonebridge International

Insurance - £8.4m• Homeserve - £30m• Debeka (Germany) - £1.3m

Challenges• Constantly evolving• Changing landscape• New demands• Existing infrastructure

doesn’t often support• Added cost• Cant deliver the

“evidence” without data analytics (or a ton of people)

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London markets ……

• Lloyds is another regulator

• Evidence adherence to business plan / policies

• Ensuring meeting capital adequacy

• Time consuming without data & analytics

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More than X numberof complaints upheld

againstan adviser

More than X% ofbusiness with one

provider.

X% of files reviewed revealed issues that

requiresignificant remedial action.

Compliance / Regulation Measures

Brokers

Commissions Binder Activity

Complaints Volume/Values Monitoring

UnderwritingLoss ratios Transparency

ClaimsSolvency Performance

Underwriting

Claims

Complaints

Financial

Domains

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So……

What are the practical challenges to delivering on Big Data & Analytics?

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Accidental Architecture … anatomy of an insurer

3rd Party Data Subscriptions

Unstructured documents, emails

Clickstream

Server logs

Sentiment, Web Data

Sensor. Telematics

Geolocation, Spatial

Existing Data Infrastructure New Data Sources

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Challenges …….

• You need to physically get access the data

• There are often ‘data guardians’

• You need a place to put it all

• Data quality will be an issue (declarations)

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But what advice on solutions

can a non-techy offer you?

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I like the “Data Lake” as a concept - C-suite does also

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Non-exec Advisor to Datalytyx

Cloud & Managed Services forBig Data & AnalyticsData Quality & MDMEnterprise Information Management

Data Swiss Army Knife forData Integration / ManagementData Quality Big Data Integration

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Datalytyx Cloud – Big Data Analytics & Performance

Datalytyx Cloud for Analytics & Performance

Customer Data Sources

PERFORMANCE / GOVERNANCE

GOVERNANCE STRUCTURE

AND DATABASE

Scorecards

Measures and KPIs

Targets & Thresholds▶ Sap

▶ Oracle

▶ Agresso

▶ Infor

▶ Concur

CORE BUSINESS DATA SOUCES

▶ Remedy, HP, CA

▶ Tivoli, Openview, BMC

▶ Project/Portfolio Management

▶ Capacity and Utilization Systems

▶ Backup/Monitoring Systems

▶ Financial Systems

▶ Cloud Systems

▶ Customer Satisfaction Systems

▶ 3rd Party System

▶ Web Logs & Click streams

▶ Machine generated

▶ Geolocation data

▶ Marketing automation systems

OTHER POTENTIAL DATA SOURCES

▶ (Spread Sheets)

▶ (CSV Export)

▶ (Text Delimited)

OTHER FLATFILE SOURCES

▶ Salesforce

▶ Netsuite

HIGH SPEED DATA ANALYTICS & DISCOVERY

DATA CLEANSING / MANAGEMENT

Custom Data Cleansing & Data Processing Rules

VariousExtract

s

VariousExtract

s

VariousExtract

s

Clean Data

Data Lake of High

Quality Data

Action, Issue, Risk, Milestone, Service Improvement Tracking

CSV

HP Vertica High Speed Analytics Tableau

Governance Scorecard

Analytics Scorecard

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Datalytyx & Talend - Finance & Insurance Customers

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Thanks & Questions