ASSK

Alternative Markets for Actuarial Science

28th November 2014Mukami Njeru

Guess Where?

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Contents

1. Core Actuarial vs. Alternative ‘Markets’

2. Developed vs. Developing Markets

3. Is Alternative Better than Traditional/Core

4. Examples…

5. Overview of Big Data and Data Analytics

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What are the Alternative Markets?

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Climate Change, Big Data & Data Analytics, Strategy,

Insurance Operations, Project Management, Infrastructure,

Payment and Incentive structures

Enterprise Risk Management, Financial Advisory, Insurance

Consulting, Education, Professionalism, Banking,

Finance

Life, Non-Life, Health, Pensions, Investments,

What should you consider?

Pros

Exciting

Chart your own path and course

Set precedents and standards

Cons

Core work will always be priority

Work stream may not be stable

Pay will also be volatile as value add may not be clear immediately

Competition from other professions

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Case Studies / Examples

Rosi Winn – Climate Change

A director at PwC Sydney Australia

Works with various donor and research organizations

Develops solutions for clients in response to key climate change

issues

Anthony Tockar – Data Scientist

E.g. Researched differential privacy, a state-of-the-art method for

ensuring data privacyOne of the posts, "Riding with the Stars: Passenger Privacy in the NYC Taxicab Dataset" has gone viral, and has been featured in

many articles around the world and has attracted comment by many

leaders in the field of data privacy.

Michael Alwright – Predictive Analytics

Applies innovative mathematical and strategic methods to develop

solutions and strategies which have saved millions of dollars across government, health, finance and

non-for-profit industries.

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Big Data – Definition

• The term ‘Big Data’ was included in an update of the Oxford English Dictionary (2013):

• Big data n. Computing : data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges;.

• 1.4.2 The following definition is outlined in Wikipedia:• Big data is a blanket term for any collection of datasets so large and complex that it

becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization

• It’s definitely a space for actuaries to ‘play’ in… (don’t raise your hand up if you like data)

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Big Data – How much information

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Americans consumed information for about 1.3 trillion hours, an average of almost 12 hours per day. Consumption totaled 3.6 Zettabytes and 10,845 trillion words, corresponding to 100,500 words and 34 gigabytes for an average person on an average day.” How Much Information? 2009

Big Data – How much information

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“The world’s servers processed 9.57 Zettabytes of information, almost 10 to the 22nd power, or ten million gigabytes. This was 12 gigabytes of information daily for the average worker, or about 3 terabytes of information per worker per year. The world’s companies on average processed 63 terabytes of information annually. “Bohn, Short, and Chattanya Baru 2010 Report on Enterprise Server Information,”

Big Data – Sources

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Characteristics of Big Data

VOLUME – amount and support structures

VELOCITY – pace of continuous flow

VARIETY – breadth of sources and types

VERACITY – Level of confidence

VALUE – aspects that ensures insightful and practical findings

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Characteristics of Big Data

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Big Data and Data Analytics

The amount of structured and unstructured data becoming available to the insurance industry continues to grow rapidly.

Analysing these large datasets can provide helpful information to avoid risks, discover new opportunities, identify customer trends and develop new products.

Big Data analysis is fast becoming the competitive, innovative edge insurers are looking for.

• Although data analysis is not new to the insurance industry, the volume and range of data being available is constantly changing.

The true value of Big Data is only realised when relevant information can be extracted rapidly and when it can be structured in a way that fact based decisions can be made based on it.

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Data Analytics – For Insurers

The traditional management approaches have been conservative and relatively unchanged over the past few decades

(Breading & Smallwood, op. cit.).

A shift is currently underway; adopting a ‘management by analytics’ approach to running their businesses.

They state that this shift, fuelled by Big Data and high performance analytics, is enabling insurers to select more profitable business, implement more precise pricing, manage the risk portfolio holistically, improve fraud detection, and increase investment returns.

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High performance analytics is an area where a strong alignment between business and IT can create powerful new capabilities within an insurer’s organisation

Insurers are now catching up in their adoption of predictive and optimisation models in business processes such as sales, marketing, and service.

They overall effect of these developments is expected to be greater depth and breadth of analytics talent throughout organisations, significant improvements in management processes, and new products that deliver greater value to customers and to society

Factors Impacting Data and Analytics

Wider issues – LegislationWider issues – Governance and Privacy e.g. target example

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Data Analytics – and Actuaries

The analytics performed by actuaries are critically important to an insurer’s continued existence and profitability.

Over the past 15 years, revolutionary advances in computing technology and the explosion of new digital data sources have expanded and reinvented the core disciplines of insurers.

Today’s big data analytics in insurance pushes far beyond the boundaries of traditional actuarial science

According to the research carried out based on 86 insurers, 71% were using analytics in the actuarial modelling and risk analysis area and 56% were using it in pricing.

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Data Analytics – Variations

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Guided Analytics Predictive Analytics

Operational dashboards & visualizations

Recommends action buckets to front-end business users with automated decision trees and algorithms

Creates value by optimizing day-to-day decisions & execution, regardless of skill

Data enrichment & predictive modeling

Recommends campaigns (individual targets, product & communication) connecting to campaign engines

Creates value by transforming marketing: better targeting, better conversions

Big Data and Data Analytics

• My interaction and experience… I am NOT an expert

SAS data manipulatuion.. Guided analytics

Deloitte Data Lab – Telematics

Sourcing for business analytics tools for CIC… but are we ready as

Kenya…

• Is this within our reach??

• It could be the new frontier for actuaries… let’s do it! Or be ready for it!

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Big Data and Data Analytics – In Practice

• [US ELECTION WIN 2012 – an extract from a presenation at the ASSA Convention 2014]

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QUESTIONS?

mukami.njeru@gmail.com