The document discusses how leveraging open data can help optimize commercial insurance processes by collecting online open data to accurately classify small and medium enterprise (SME) client activity, industry codes, and locations in order to reduce errors in submissions and policy information that result in improper premium calculations. It proposes using this data during underwriting to quickly identify risks and unwanted clients, as well as before renewals to discover hidden risks and increase premiums accordingly. A case study example shows how one insurance carrier was able to prevent 31% premium leakage and increase revenue by 4% by using this open data to correctly identify SME client activities and addresses.

1. Leveraging Open Data to
optimize Commercial Insurance
processes

2. 47%
of SME activities are wrong due
to submission manual mistakes and
lack of data updates during the
life of the policy
COMMERCIAL INSURANCE
What is the
Problem?

3. Collect SME online open data to classify
correctly activity, SIC codes and location
COMMERCIAL INSURANCE
Our solution

4. Business
Name &
Address
In real
time
Coordinates &
UPRN
Risk Address
Business
Name
SIC
Codes
Activity
Description
COMMERCIAL INSURANCE
How it works

5. Marylebone Car
Centre
Activity Sale of vehicle
parts
Sale of used
cars
SIC N/
A
3315
Premium* $
1,080
$ 1,382
(+27%)
Address 68 Highfields Rd,
Witham CM8 2HJ
62 Balcombe
Street, London
NW1 6NE
Before After
Use online data to discover
hidden risks in existing
clients before renewals.
Increase premiums when risks
are higher and deny unwanted
risks.
KPIs
1. GWP Increase
(%)
2. Loss
Reduction
NOTE Wenalyze does not calculate the premium but provides the data to do
so more accurately.
COMMERCIAL INSURANCE
Data Analysis
Process

6. Before 2
hours
Implement API to ingest
submission data during
underwriting processes.
Reduce quoting times and
reject unwanted risks.
KPIs
1. UW Saved
Time (%)
2. Loss
Reduction
Website
Name
Address
Tax ID
Activity
Now 1
Min
COMMERCIAL INSURANCE
Underwriting
Automation

7. 93%
of SMEs
DATA IS ENRICHED
IN
COMMERCIAL INSURANCE
Average Data
Density

8. COMMERCIAL INSURANCE
Markets &
Clients

9. Scalable by Size
& Region
Real-Time
Collected Data
Flexible
Architecture
Average Data
Density
COMMERCIAL INSURANCE
No IT implementation required

10. CASE STUDY
SME Industry Activity Classification

11. Portfolio data with 42% SMEs
of activity and/or address
updates
Top 3 P&C Spanish Carrier
with 50,000 SME clients
CARRIER DELIVERY
31% Premium Leakage
Prevented
GWP Increase of €2.4 M (+4%)
BENEFITS
Clients with bigger risks were
repriced before renewals
ACTION
CHALLENGE
Activity and address changes in SMEs are not usually identified unless manual action
is taken. Additionally, submission inaccuracies are often hidden. The carrier asked
Wenalyze to collect open data to automatically identify correct activities and
addresses.
PRODUCTS
Liability and Property Insurance
ROI
x7
CASE STUDY
Top P&C Carrier uses Wenalyze to Correctly Identify SME
Activities

12. Roger
Ferrandis
COO & Co-
Founder
/in/rogerferr
andis
roger@wenalyze.
com