Make Smart Use of Available Data1st Catastrophe Knowledge Exchange

5. July 2016 - B. Aeberhardt & M. Spörri

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

Beat Aeberhardt

Head NatCat Tools Group UW

Beat_Aeberhardt@Swissre.com

Martin Spörri

Head P&C IT Info Foundation & Smart Analytics

Martin_Spoerri@Swissre.com

Who are we?

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Introduction

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

New Insights

Large Datasets

Visualization

Combine internal with

external data

Where’s the smartness in using available data?

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processing large and full datasets at reasonable speed and cost

combining internal with external data on a ‘good enough’ basis rather than perfect matches, validated through smart plausibility checks

… deriving new insights by:

investing in value driven and tailored visualization

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri 5

Importance of DataSmart usage of available data boosts the performance of an organisation

Source: "Infographic based on data from Columbia Business School, MIT/Wharton, IBM, Forrester Research, CMO Council/SAS"

The Problem

The Impact

Internal data is often unused!

Tapping into the data boosts financial profit, return on investment and productivity!

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

• Loads of detailed information on property risks available

– used mainly in individual costing process

– received data granularity increasing

– stored in internal NatCat platform used for costing, RM and capital allocation for all BU’s

– NatCat exposure is fully linked with all (re-)insurance structures and contracts

• Granularity of NatCat models is increasing

– Geographic resolution

– Improved handling of financial structures

– New physical properties becoming more important

What does this mean for NatCat?

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Internal data is often underused!

Demand for ‘smarter’ use of exposure data is increasing

Cluster DB

NatCat Exposure Data Intelligence

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Goal & Motivation - Current situation

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Billions of Risks

Stored internally

Clients participating

Incoming channels

•Broker

•Direct

•Global vs R&N

Years

Same risk is stored multiple times in our system

• several clients participate in the same risk

• we participate on same risk via different channels

• through renewals, we receive ‘same’ risks year over year

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

What is a cluster?

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Key properties of a cluster are:• Distribution of an attribute value (primary modifiers;

Structure types, Occupancy, Design Year, …)

• Hierarchies of clusters

• Life cycle of a cluster

Pre-Cook

Lookup & Enrich

Periodical Updates

Detailed Cluster Aggregated Cluster

Grid Match Fix Confidence

Index Threshold

Include new Exposure data Re-calc clusters

Histogram

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

Costing

•Exposure validation & reporting

•Shadow rating in NatCat models

Accumulation

•Supporting risk clearing

•Accumulation of Non-NatCat LoB

NatCat models

•Granular building stock improves model assumptions for Geo and Vulnerability disaggregation

Analytics

•Benchmark reports

•Protection gaps, neighbourhood analysis

•Temporal exposure development

Where can the Cluster DB be used?

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1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

Validation of derived

Cluster DB

•e.g. sample check of important clusters

•e.g. validation on aggregated basis and benchmark with external datasets

False positives

•e.g. wrong allocation of risk objects to clusters (e.g. inconsistent street address information

Relation of clusters

•e.g. apartment to building, building to complex/campus

Context specific

bias

•e.g. data tuned/optimized for given peril and/or specific model vendor

•e.g. combination with other data sources

Challenges faced

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Size ofDataset& Bias

All faced challenges are related to the sheer size of the exposure dataset we are working with and the fact that the submitted information is erroneous.

1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

•Geographic extension; CA/FL USA Europe Global

•Attribute extensions: more physical properties, insured values and coveragesScope

•Extend to other Lines of Business (e.g. Terror, Fire)

Models

•Owner of physical objects (e.g. company names)

Ownership

•Market building stock and simple “market portfolios”

•CBI networkDerived products

•New data sources from IoT (e.g. connected homes)

•Google 3D

•Sanborn DBEnriching

Roadmap

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1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

• Do you leverage internal data beside using in the context of costing?

• If yes, what are the areas applied?

• Do you track individual physical objects over time and across policies?

DiscussionAre you doing something similar?

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1st Catastrophe Knowledge Exchange | B. Aeberhardt | M. Spörri

Legal notice

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The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation.