Big image analytics for (Re-) insurer

BIG IMAGE ANALYTICS FOR (RE-) INSURER
FLAVIO TROLESE
4QUANT | BIG IMAGE ANALYTICS
THURSDAY, APRIL 7 2016

4Quant | BIG IMAGE ANALYTICS
BIG DATA IMAGE ANALYSIS BIG IMAGE
ANALYTICS
REMOTE SENSING
POTENTIAL (RE-)
INSURANCE CASES
Q&A

[...] Data sets that are so large or complex that traditional data
processing applications are inadequate.
Challenges include analysis, capture, data curation, search,
sharing, storage, transfer, visualization, querying and
information privacy.
BIG DATA
https://en.wikipedia.org/wiki/Big_data

BIG DATA
Optimize Funnel Conversion
Behavioral Analytics
Customer Segmentation
Predictive Support
Market Basket Analysis and
Pricing Optimizations
Predict Security Threats
Fraud Detection

IMAGE ANALYSIS
Tools and technologies (for analysis of «small (image) data»)
(limited) services for «big (image) data»

Linearly scalable, interactive, fault-tolerant big image data
processing technology for two-, three- and four-dimensional
image data.
Processes tera- to petabytes of image and video data in near
real-time and at low costs
+ IMAGE ANALYSIS
+ BIG DATA
= BIG IMAGE ANALYTICS

We do image analytics on Big Data → BIG IMAGE ANALYTICS
Our mission is to help companies monetize their image and
video content.
4QUANT

Problem
«Analyze phenotyping and
genetic linkage of cortical bone
microstructure in the mouse»
Terabytes of image data
Analysis of image data takes years with existing
(small data) image analysis tools
Solution
«Use a Big Data Framework in combination
with image analysis algorithms»
BIG IMAGE ANALYTICS PLATFORM
Linearly scalable, interactive, fault-tolerant big
image data processing technology for two-,
three- and four-dimensional image data
Tera- to petabytes of image and video data
Near real-time processing
TECHNOLOGY
«High-throughput phenotyping and genetic linkage of cortical
bone microstructure in the mouse»
K. Mader, L. Donahue, R. Müller, M. Stampanoni; BMC
Genomics 2015 16:493;
«Moving image analysis to the cloud: A case study with a
genome-scale tomographic study»
K. Mader, M. Stampanoni; AIP Conf. Proc. 1696, 020045 (2016)

2014 20162015
HISTORY
PhD
Thesis
ETH Pioneer
Fellowship
Talk at Spark
Summit
ETH Lecturer
Assignment
Invited Big Data
Workshop
Werner-Meyer-
Ilse Award
Databricks
Partnership
IBM Partnership
Partnership
Partnership
COPD Study
NPC Study
2013

Veracity
Tested validated correctness
Fully traceability of all algorithms
Variety
For two-, three- and four-di-
mensional image data
Supports / parallelizes existing
single CPU algorithms (e.g.
ImageJ, Matlab, R)
Velocity
Stream-based analysis in
almost real-time
Ad-hoc queries
Throughput of > GB/s
Volume
Linearly scalable beyond
gigavoxels and tera- / petabytes
Elastically scale to available
resources
Value
Detection of anomalies, intelligent
feature recognition, tracking of
millions of features, smart
segmentation, parameter sweeping,
pattern recognition
BIG IMAGE ANALYTICS PLATFORM
Variety
Finds insights from complex, noisy,
heterogeneous, longitudinal, and
voluminous imaging data.
Answer questions that were
previously unanswered.

Flavio Trolese / CFO
Corporate Finance
HR / Administration
Dr. Kevin Mader / CTO
R&D
Product Development
Scientific Consulting
Joachim Hagger / CEO
General Management
Business Development
Fundraising
MANAGEMENT TEAM
M.Sc. in Physics ETH
Co-founder and managing partner of Netcetera, an IT
consulting company, 390 employees
BScIT at ZHAW
Co-Founder and managing partner of Panter AG, an IT consulting
company, 40 employees; Co-Founder and managing partner of
Colab Zurich / ImpactHub Zurich (> 350 Members)
PhD on “High-throughput, synchrotron based tomographic
microscopy tools” at ETH/PSI
ETH Pioneer Fellowship, Lecturer in the X-ray microscopy group
D-ITET ETH Zurich

Transportation Material screening
INDUSTRY&
TECHNOLOGY
PathologyRadiology
Image intelligence
Pharma
Remote sensing Human swarms
Imaging CRO
GEOIMAGING
MEDICINE&
PHARMA

EXAMPLES
Video-to-Text Change detections
Measurements Track monitoring

MEDIA

REMOTE SENSING
New, exciting sources of images with meter resolution,
updated almost daily
225 GB / y 333 TB / y 333 TB / y 15 PB / y

REMOTE SENSING / BIAP
Meaningful Results
BIAP
Petabytes of
satellite image data
e.g. flood risk calculations
4Quant’s BIAP
VM VMVM VM
VM VMVM VM

Integrate Deep Learning Algorithms to automatically
identify unknown, or changing regions in the image
INTEGRATION OF MACHINE INTELLIGENCE

Utilize high-temporal resolution
and decades of archives...
… to see new insights in the data and extract
quantitative results for building new models
HISTORICAL CHANGES IN SURFACE PATTERNS

Satellites produce lots of data. What can we do with it?
Data courtesy of Planet Labs
Location and size
of water-features
Distance to nearest
water feature
Building location
Identifying
High-Risk areas
TRANSFORM IMAGE DATA INTO MEANING QUANTITIES

REAL-TIME DAMAGE ESTIMATION
Often after a major incident (hail storm, fire, flood), experts
are required to assess the overall lost value. A timely and
accurate loss assessment helps insurers to allocate the right
amount of provisions in time.
Using satellite and drone images, 4Quant accelerates and
improves the process of loss assessment. Automated
analysis of the damaged assets may be both faster and
more reliable than the traditional, manual assessment.
This helps insurers to allocate resources more precisely
both in volume and time.

ACCELERATION / IMPROVEMENT OF CLAIMS MODELING
Fast and reliable information on damages is also crucial for claims
modeling. Modeling firms undertake huge efforts to investigate
damage events to improve their predictive capabilities.
As 4Quant may help insurers in
assessing insured events, we may also
support modeling firms with a thorough
first-level event assessment. Using event
imagery analytics enables modelers to
base their equations on a growing base
of robust data.
This in turn helps insurers to source
improved severity curves in ever more
reliable predictive hazard models.

➔ Our trained algorithms calculate the
expected claims in real time with great
accuracy
➔ Results can be directly integrated into
insurer’s claims management system
ONLINE CLAIMS MANAGEMENT FOR CUSTOMERS
Example: effective car insurance claims handling
Process:
➔ After an accident, the driver or garage
makes pictures of the damaged car
➔ Our database knows the topology of
the healthy car

REAL-TIME INFORMATION FOR INSURANCE LINKED
SECURITIES
Combining trained algorithms with real-time satellite imagery,
4Quant develops models for real time hurricane analytics.
This enables 4Quant to provide value for insurers as the
sponsors of Cat Bonds, and also for investors, asset
managers, and rating agencies.
Parametric Cat Bonds are indexed to the natural
hazard caused by nature, such as atmospheric
pressure in the eye of a hurricane.
Rapid assessment of such parameters is key to
enable liquid markets and effective risk transfer.
However, often parameters are not available in
real time.

INSURANCE MODELS
Worldwide Loss Events 1980-2013
2014 Mü nchener Rü ckversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at
February 2014

ACCOUNTING FOR CLIMATE CHANGE
Climate Change Predictions
2014 Mü nchener Rü ckversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at
February 2014

2014 Mü nchener Rü ckversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at February 2014
ACCOUNTING FOR CLIMATE CHANGE

IMPACT OF CLIMATE CHANGE
2014 Mü nchener Rü ckversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at February 2014

Image-supported premium adaptation of car insurances

ADVANCED ANALYTICS: INNOVATION DRIVER IN INSURANCE
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Big image analytics for (Re-) insurer

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