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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