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