Transforming Insurance Operations through Data and Analytics
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Transcript of Transforming Insurance Operations through Data and Analytics
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Transforming Insurance Operationsthrough Data and Analytics
Roger Oldham
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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