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Manage fraud risk & compliance across your entire insurance company

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<strong>Manage</strong> <strong>fraud</strong>, <strong>risk</strong> & <strong>compliance</strong><br />

<strong>across</strong> <strong>your</strong> <strong>entire</strong> <strong>insurance</strong> <strong>company</strong><br />

Philip van Waning<br />

Data Partner <strong>Manage</strong>r<br />

Friss<br />

Filip Champagne<br />

Business Consultant<br />

Bisnode


FRISS<br />

<strong>Manage</strong> <strong>fraud</strong>, <strong>risk</strong> & <strong>compliance</strong><br />

<strong>across</strong> <strong>your</strong> <strong>entire</strong> <strong>insurance</strong> <strong>company</strong><br />

Philip van Waning<br />

Data Partner <strong>Manage</strong>r


Supporting honest <strong>insurance</strong>


60+ clients in 12 countries


Risk Assessment at Underwriting


Fraud Detection at Claims


Some <strong>risk</strong>s should not be accepted.


Trends in de verzekeringsmarkt


Controle door premiestelling<br />

• Premie en bijbehorend risico bepaald in de<br />

premieberekening<br />

• Acceptatie regels voor de risico’s welke niet<br />

beprijsd zijn.


Het “onverwachte”………<br />

• Met externe bronnen en marktkennis<br />

geautomatiseerd het “onverwachte” te detecteren


Indicatoren / Metingen<br />

Interne en Externe Bronnen<br />

Risico gebieden<br />

Wanbetaling<br />

Kredietwaardigheid<br />

Schade (verleden)<br />

Polis (verleden)<br />

Betrouwbaarheid en gedrag<br />

Voertuig en object<br />

Zakelijk gebruik<br />

Leefomgeving


FRISS Detectie<br />

Expert<br />

Model<br />

Profiel<br />

Model<br />

Voorspellend<br />

Model<br />

Social Network<br />

Analyse<br />

Werkwijze gebaseerd op<br />

kennisregels<br />

Werkwijze gebaseerd op<br />

“normaal gedrag” in<br />

profielen<br />

Voorspellende modellen<br />

gebaseerd op historische<br />

data<br />

Netwerken op basis van<br />

Link Analysis<br />

FRISS<br />

score


Voorbeelden<br />

• Taxi bedrijf gevestigd op adres van de<br />

verzekeringnemer<br />

• Geimporteerd voertuig met een hoge diefstal kans<br />

• Moraal van verzekeringnemer is laag<br />

• Patroon van wanbetaler in de polisaanvraag


Loss ratio<br />

49% 74% 89% 112% 138%<br />

45000<br />

40000<br />

35000<br />

30000<br />

25000<br />

20000<br />

15000<br />

10000<br />

5000<br />

SPR<br />

0<br />

0 1-35 35-80 80-130 130+<br />

Series1 16091 40572 9238 4313 934<br />

Score<br />

Number


29-11-2016<br />

MOVING ON TO BISNODE...


Agenda<br />

• Inform<br />

A small word on Bisnode<br />

• Research<br />

How do we look upon some of<br />

the <strong>insurance</strong> industry’s challenges?<br />

• Learn<br />

Some answers….


Inform<br />

A small word on Bisnode


Bisnode Group<br />

B I S N O D E<br />

18C O U N T R I E S<br />

150 000<br />

c l i e n t s<br />

HQ Bisnode Group<br />

Solna, Stockholm<br />

375<br />

€ million<br />

revenue in Europe<br />

EXPERTS<br />

2400


Mission Bisnode Belgium<br />

WE HELP YOU<br />

TO KNOW YOUR CUSTOMERS,<br />

TO MINIMIZE RISKS AND MAXIMIZE GROWTH<br />

BY TAKING<br />

SMART DATA<br />

BUSINESS DECISIONS.


Bisnode Belgium<br />

+40YEARS<br />

e x p e r i e n c e on<br />

B e l g i a n m a r k e t<br />

10 years<br />

experience on<br />

European market<br />

+800<br />

c l i e n t s<br />

€ million<br />

30revenue in Belgium<br />

185<br />

EXPERTS


Bisnode Belgium<br />

1975<br />

2006<br />

2013<br />

SOPRES<br />

(B2C)<br />

2004<br />

Wegener DM<br />

WDM Belgium<br />

2008<br />

Bisnode<br />

+ Bisnode Interact<br />

(international solutions)<br />

+ Spectron + Biside (analytics)<br />

(former Ketels DM,<br />

Vicindo)<br />

(B2B)<br />

Business Solutions<br />

(B2B)


Our data sources<br />

Socio-demo<br />

Lifestage<br />

Lifestyle<br />

Intentions<br />

Behavioural<br />

80% 84%<br />

B2C<br />

Off line Online<br />

B2B<br />

Off line Online<br />

Company info<br />

Financial info<br />

Decision makers<br />

individuals<br />

+18 years<br />

+ 1 000 000<br />

e-mail addresses<br />

households<br />

million<br />

<strong>company</strong><br />

1,8addresses<br />

+ 345 000<br />

<strong>company</strong> e-mails<br />

+275 000<br />

e-mail position level<br />

100% compliant with privacy & opt-in legislation


Research<br />

The industry’s challenges:<br />

2 cases


Case I<br />

Moving into digital


Moving into digital<br />

Opportunity<br />

There’s<br />

BIG<br />

Opportunity


Moving into digital<br />

Data<br />

There’s<br />

BIG<br />

data


But what about <strong>your</strong> customer relation?


Case I<br />

Moving into digital<br />

…1001110001000010...<br />

The Bottom Line:<br />

Your CUSTOMER becomes a digital identity = DATA


My customer is Data! Brilliant!


My customer is data!<br />

Now I can…<br />

Install software to capture all necessary data:<br />

And (finally) get some real Customer Insights!<br />

1.<br />

Behaviour<br />

2.<br />

Lifestyle<br />

3.<br />

Risk<br />

4.<br />

Socio-<br />

Demo<br />

5.<br />

….<br />

Single Customer View


Software does the trick<br />

Are you sure?<br />

Capturing data on <strong>your</strong> customer means uniquely IDENTIFYING<br />

accross all channels<br />

Operational<br />

systems<br />

ERP<br />

Customer<br />

service<br />

Social<br />

listening<br />

Inbound<br />

email Agents Apps Website<br />

Identify <strong>across</strong><br />

all channels


Software does the trick<br />

An example<br />

Are these data customers or prospects?<br />

Do they represent a high or low Risk?<br />

Service Center<br />

Webform<br />

Email message<br />

Schepers<br />

Schepers<br />

Schepers<br />

H.Schepers@live.be


Case II<br />

Silos


Data Silos are not Evil!<br />

Silos sometimes have a very good reason to exist<br />

- Acquisitions<br />

- Too high cost for changing IT architecture<br />

- ...


Data Silos are not Evil<br />

Silos just need good bridges<br />

(and a master silo...)


Not Evil but not convenient either<br />

Some simple everyday questions<br />

- How many Policies does Mr. Janssens have? What’s his Lifetime Value?<br />

- What exactly is the <strong>risk</strong> related to Mr. Janssens and his family?<br />

- What about the link between Mr. Janssens, Ms. Peeters and JanPeet BVBA?<br />

Let’s ask IT!


Not Evil but not convenient either<br />

Some less everyday question<br />

How can I organize the Single Customer View of customers after an acquisition<br />

or merger?<br />

What is the cost of the lost opportunity & increase in <strong>risk</strong> if I don’t<br />

merge records that should be merged?<br />

What is the cost of the customer impact if I merge records that shouldn’t<br />

be merged?<br />

Let’s ask IT!


Not Evil but not convenient either<br />

An example<br />

- Are these people the same individuals?<br />

- Do they represent the same household? The same <strong>company</strong>?<br />

Van Naren Jenny<br />

Van Haren


Learn<br />

Some answers


Ex. 1 : Too much overkill in software<br />

Schepers<br />

Schepers<br />

Schepers<br />

H.Schepers@live.be<br />

• How do we solve this?<br />

• Using exhaustive consumer reference data<br />

• What’s the <strong>risk</strong> if you apply software matching?<br />

• A software would identify the records as a match with high probability<br />

• In fact, the 2 ‘Guy’s are 2 different people:<br />

54<br />

• one is identified at a different address<br />

• the other is confirmed at the address<br />

• The email is linked to the 2nd Guy<br />

• High <strong>risk</strong> of coupling 2 accounts that shouldn’t be coupled!!


Ex. 2 : Too much underkill in software<br />

Van Naren Jenny<br />

Van Haren<br />

• How do we solve this?<br />

• Identification through movers reference data since more than 20 years<br />

• Exhaustive consumer reference data<br />

• What’s the <strong>risk</strong> if you apply software matching?<br />

• A software couldn’t possibly identify the first 2 records as the same<br />

• A software couldn’t possibly identify all records as part of the same household, and the same<br />

<strong>company</strong><br />

55


3 step program<br />

1 2 3<br />

Identification<br />

Single Customer<br />

View<br />

Cleaning/enrichment


Typical Implementation<br />

Silo/Channel1<br />

Silo/Channel2<br />

Silo/Channel3<br />

Silo/Channel4<br />

…<br />

New Records<br />

Client<br />

Qualify<br />

Golden Records<br />

+<br />

Data Quality<br />

+<br />

Bisnode Key’s<br />

Your platform, Bisnode’s platform (DataHub) or hybrid<br />

Bisnode MDM<br />

Change<br />

Track


Learn<br />

One last take-away


The value of covering 90%<br />

In a world of automation & real-time,<br />

even only 1% decrease in Single Customer View generates<br />

a multitude of irrelevance & <strong>risk</strong><br />

- Treating <strong>your</strong> own customers as prospects<br />

- Cross selling to clients that already own the product<br />

- Missing out on opportunities (eg. Not knowing that <strong>your</strong><br />

customer has 3 <strong>insurance</strong> policies instead of 2)<br />

- Missed Internal Black List matching (on household or<br />

B2B2C level)<br />

- …


Filip Champagne<br />

filip.champagne@bisnode.com<br />

+32 477 774 293<br />

Philip Van Waning<br />

philip.van.waning@friss.nu<br />

+31 6 57 58 90 94<br />

THANK YOU

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