Insurance Innovative Solutions 2016
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Transcript of Insurance Innovative Solutions 2016
A digital age vision for insurance services
Raising the Bar!!
©2015 • Chiara Zambelli- Pietro Marinelli• 5 November 2015
Agenda
What keeps insurers awake at night?
Modernisation Combining the data souces Changing the paradigm Machine Learning Innovating with our partners We shall deliver..
©2015 • Chiara Zambelli- Pietro Marinelli • 05/11/2015
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What keeps insurers awake at night?
Client Understanding• Can Insurance companies do a better job of identifying and valuing
their better and worst customers?• How can Insurance companies innovate using media and other
communication channels to acquire new customers or deepen their relationship with the existing ones?
Strategy & Growth• How will changing consumer socio-economic and
demographic forces impact for Insurers products?• How will key macro economic and regulatory changes
impact growth and opportunities ?
Customers
Regulation
Economy
What keeps insurers awake at night?
Fraud mitigation• One of the biggest areas where insurers suffer of
enormous expense line.
Sales & Distribution• How should Insurers companies improve the customer
experience through each distribution channel to maximise sales and profit?
• Can be the pricing model optimised by capturing new data to apply to underwriting process?
Crime
Modernisation Process
Change of mind set is required• From the legacy technologies used towards new emerging
technologies• From a claims leakage process that is reactive to one that is
proactive—potentially leading to enormous potential savings.
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Combining the data sourcesTraditional data Non-traditional
Unstructured
Web Sentiment
POQ - POS - MTA - FNOL
Customer declared data
Emotional context
LinkedAddresses
Intre
Integrate
Anal
yse
Visualise
Discover
CreditBureauExternal data
IDV ClaimsHistory
Vehicle Fraud
Disparate data
Legacy systems
Telematics
Changing the paradigm
The UnknownPreviously unknownmetrics revealing underlying trends and patterns driving new questions.
The KnownRapid multilayer analysis utilizing big data analytics techniques.
What are the most common words in policy holder injuries description?
©2015 • Chiara Zambelli- Pietro Marinelli • 05/11/2015
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Let the data reveal it to us with a cool visualisation of the words appeared in the claims
Machine Learning - Predict!
Questions for the ML• Will this policy holder have an accident?• How much will be the refund of a given claim?More complex• Given telematics data about two trips in different cars, can you
say that the driver is the same?
Automatic design models from dataIf you can automate the reasoning behind a model built by a human you can replicate his effort as many times as you want and with a much smaller amount of time..
Innovating with our partners..
In association with…….
“ecosystem”
Insurance apps
Unstructured
Images
Telematics
Traditional dataClaims
Vehicle
Credit
Fraud
Locational
Revolutionary Analytics tools
Device led data
PsychometricProfiles
Telematics
+
• Internet identity• Pre-validated profile
Rich Data Sources
• Intentions• Hopes• Fears• Feelings
• Telematics
We shall deliver….
Many more hidden insights from existing data! to drive previously unasked questions…………...
Legal entitlement
DPA section 7
Improved customer on-boarding journeys.Commercial
Digital passportTelematics
Fully Compliance Services
More accurate dynamic pricing
Real time scoring techniques
Data visualisation
Operational Improvement to reduce human failure
Pattern Detection on Customer Behaviour
Improved Customer service by KYC analytics
Emotional ContextCrif Footprint
Thanks for your attention
©2015 • Chiara Zambelli- Pietro Marinelli• 5 November 2015