Bruce McKee

Microsoft, Financial Services

Bruce.mckee@microsoft.com

@bajmckee

The Internet of Things, will it change insurance forever?

Grow premium Improve investments returns

Price policies effectively

Address complex regulations

Spur product & service innovation

Attract a new generation

What

opportunities

& challenges

do these create?

Major themes and challenges

I1995 I2005 I2010 I2015

T E LEPHONE

D I RECT S E LL ING

I NTERNET

D I S INTERMEDIAT ION M 2 M

D I SAGGREGATI ON

I OT

P E R ONALISED

I N S IGHT

The history of insurance

What is it? Ability to offer Telematics pre and post-policy

Always on

Data capture for smartphone / ODBII and black box

Uses Microsoft Azure for flexibility

How can it be used? • New Telematics based

products

• Understand key influences on driving behavior

• Price and manage risk better

• Provide regular touch-points with consumers

• FNOL / Claims

Examples:

• Aviva

• Only Young Drivers

• Catlin

Aviva – 500,000 app downloads

Raised NPS

Best ever campaign

Aviva Drive

Fixed device

Costs – device, install, SIM, data, analysis

~Euro 200

ODBII Plug in device

Costs – device, data, analysis

~Euro 100

Smartphone – FNOL, drive profile

Costs – App, Data, Analysis, Service

~Euro 50

Telematics devices

Telematics devices Vehicle trips

Automatic, geo-fence or manual

Accident reconstruction (second by second)

Drive behaviour Risk policy

Feedback

Accidents FNOL, trigger and detail (5min)

Management reporting

Connected cars

What is the Internet of Things?

Inaccessible data

Limited flexibility

Incompatible

with infrastructure Unreliable service

Slow implementation Unsecure data and assets

Connect Extend Configure Administer Harness

Machine system

What systems already exist?

All systems have different data formats

How do you

aggregate

information?

Who

controls the

data?

Which

systems

contain

valuable

data?

All business processes follow the same pattern

Step 1 Step 2 Unknown Finish

What if system data

could remove

uncertainty?

Early warning /

intervention

Enable proactive risk

management

New sensors

5 Customer insight and communications

Connect &

Collaborate

Discover

&

combine

Model &

analyse

Visualise &

Interact

OBSERVE

BEHAVIOURS

& DEFECTS

OPERATIONALISE

CREATE HYPOTHESES

TEST & ANALYSE THEM

REFINE &

OPTIMISE

Feedback

Profile

Enhance

data

DATA

RECOMMENDATION

ACTION

Existing

Intelligence

practice

Risk insight

Operational

enhancements

Insight from inference

Insight

Compare

ML Studio and the Data Scientist

• Access and prepare data

• Create, test and train models

• Collaborate

• One click to stage for

production via the API service

Business users easily access results: from anywhere, on any device

HDInsight

Desktop Data

Azure Storage

Mobile Apps PowerBI/

Dashboards Web Apps

ML API service and the Developer

• Tested models available as an url that can be called from any end point

Azure Portal & ML API service and the Azure Ops Team

• Create ML Studio workspace

• Assign storage account(s)

• Monitor ML consumption

• See alerts when model is ready

• Deploy models to web service

Machine Learning — from data to information

Churn

analysis

Equipment

monitoring

Predictive fail

rates

Active risk

management

Recommendations

Fraud

detection

Image

detection &

classification

Forecasting

Anomaly

detection

Using past data

to predict the

future

What can machine

learning do for

your business?

Customer insight

History of client contact

Risk information / profile Communications /

marketing /

Insight is no use unless you can do

something useful with the information

and take positive action as a result

Insight

Single client view /

integration of multiple

systems

Bruce McKee

Bruce.mckee@microsoft.com

https://www.facebook.com/financialservicesuk

uk.linkedin.com/in/bajmckee @bajmckee

Working Lunch series