Chief Analytics Officer Fall USA 2017 - Kimberly Holmes - XL Catlin

Analytics and the Insurance

Business Model of the Future

October 2, 2017

© 2017, XL Catlin companies. All rights reserved. I MAKE YOUR WORLD GO 1


AGENDA

• Insurance model of the future

• Analytics operating model

• Q&A


AGENDA



• Q&A

Insurance Innovation is Driven by Analytics

Analytics is at the core of new sources of

efficiency, insight, customer experience and

value-add © 2017, XL Catlin companies. All rights reserved. I MAKE YOUR WORLD GO 4

Transforming Insurance

Customization of technology, data and process

Risk-by-risk underwriting

Qualitative judgment only

Manual data capture

Scale through people

Know our policy

Sell risk capacity

Siloed systems

Disconnected data

Modularization / standardization

Portfolio underwriting

Qualitative + quantitative

Automated data capture + enhancement

Scale through technology

Know our customer holistically

Sell diversified services

Accessible connected data

Insight on demand

Underwriter skillset

evolution

Long-term strategic

technology + data

investments

It Takes Capabilities Beyond Analytics


New

Insights

Automated

Real Time Data

Automated

Model Trigger

Reporting

• New data, both internal and external, and new uses of existing data

• Analysis of business decisions

• Testing of conventional wisdom and hunches

• Internal and external data populated in real time

• Typically no manual data entry

• No gaming the system

• Minimal disruption to process

• No opting out

• Model is “invisible”

• UI automatically delivered

• Real time monitoring of decisions

• Drill down capabilities

• Accountability and transparency

• Fosters portfolio view for line underwriters

• The discipline of measuring decisions


AGENDA



• Q&A

The Core of Innovation is Understanding What

Capabilities Your Stakeholders Need

Successfully Innovate With Analytics by

• Identifying and prioritizing the capabilities needed by stakeholders

and we design and build the capabilities

• Defining and agreeing on, in advance, what metric we will improve

• Considering business context (# records, signal, availability/quality of

data) when designing solutions

• Understanding what is good enough; balancing speed of delivery with

improvement in business results

• Relentlessly focusing on change management, process and people

• Collaborating with underwriting, claims, actuarial, IT and finance –

multidisciplinary teams are more successful

8 © 2016, XL Group Ltd. All rights reserved. I MAKE YOUR WORLD GO

9

Analytics Organization

Team Lead

Business Liaison(s) Machine Learning

Steering Committee

Program Management

Information and Data

Management

Evangelize analytics

Set analytics roadmap

Opportunity

Identification &

Assessment

Capability Development

and Support

Analytics Standards and

Education

Liaise between modeling

and business

Identify viable solutions

for

Assess business impact

Project Delivery

Feature engineering

Model design, selection,

testing, build, validation

Heat maps

Implementation support

Acquire, reconcile,

profile, match data

Build relational data

model

Manage technical

capabilities – software,

model monitoring,

reports

Liaise with IT

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Opportunity Identification Process

Identify

Understand Define Assign Align

Business Initiated

Business communicates opportunities to

enhance analytics through various channels:

Intranet submission

On-going interactions with Analytics

Analytics Initiated

Analytics proactively identifies opportunities to

leverage analytics for the business through:

Mining data to develop preliminary insights

Collecting market / competitive intelligence

Business & Analytics Coordinated

Business & SA identify opportunities to

enhance analytics through:

Scheduled business working sessions

Collaborate with Business

Actuary to understand existing

analytical tools

Collaborate with Senior

Underwriter(s) to understand the

underwriting decision process

and performance of book

Coordinate with Business

Actuary to define and consolidate

project opportunities

Assess feasibility and impact on

business

Align with Business Actuary to

assign opportunities to either SA

or the business

Assign projects to SA that are

transformational2 or that the

business does not have the

resources to execute

Evaluate opportunities

Collaborate with Line of

Business Lead to prioritize

projects for SA to execute

Develop high-level project

description3

History of model development

Data sources and uses

Underwriting decision criteria

Tool support for underwriting

Profitability issues

Feasibility

Impact on business

Transformational capabilities of

opportunities

Resources to execute

Impact on business results

Estimated resources required

from business

Risks

Timeline

1

2 3 4 5

Key A

cti

vit

ies

Key

Co

nsid

era

tio

ns

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Opportunity Assessment Process

Prioritize Select Confirm

Consolidate opportunities across channels

and business units

Review and validate completeness of

project descriptions1 for each potential

opportunity

Leverage the prioritization framework to

assess and prioritize each opportunity

Select opportunities to pursue as projects

Communicate to key stakeholders that

either the project is waiting final

confirmation or the project was not selected

to progress

Confirm project selection

Communicate selected projects to Steering

Committee

Communicate project selection or deferral

to key stakeholders

Data readiness

Business resource availability

Profit impact

Project timing and duration

Analytics resource capacity

Alignment with enterprise strategy and key

priorities

Key A

cti

vit

ies

Key

Co

nsid

era

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Prioritization Framework

Resource Availability D

ata

Re

ad

ine

ss

Low

Low High

Hig

h

Lo

w

Size of Premium Impact High

Prioritization Illustration Key Criteria

Medium

1. Data Readiness

Evaluate internal and external data availability, applicability

and accessibility

Rate data readiness on a scale from low (data will be

difficult to obtain, cleanse and / or link) to high (data will be

easily accessed, is ready for analysis and / or can be linked

to internal databases)

2. Business Resource Availability

Assess business willingness to work with Analytics and

availability of resources to dedicate to the project

Rate business resources availability on a scale from low

(business is not open to working with Analytics and is

unlikely to dedicate resources) to high (business is open to

partnering with Analytics and will dedicate the appropriate

number of resources)

3. Size of Premium Impact

Estimate the premium impact from loss ratio improvement

and / or premium growth resulting form the implementation

of the analytics solution

High impact opportunities that have accessible data and willing business

counterparts will be prioritized and selected to move forward.

Effectiveness Measurement Framework

To measure and communicate the impact of SA, it is critical to identify, track and

report on key metrics that align with the organization’s strategy.

What to

Measure

Every project begins with identifying what metric we want to change. Top line,

loss ratio or expense ratio

Commitment Written agreement on changing metric agreed in a project charter which keeps

all stakeholders aligned to the same outcome.

Identify Driver

of the Metric

Identify specific actions that need to happen (or change) in order to bring

about the improvement in the targeted metric

Measure

Early – measure if action is happening

Results – indications that the targeted metric is changing

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AGENDA



• Q&A

Data & Analytics

Chief Analytics Officer Fall USA 2017 - Kimberly Holmes - XL Catlin