07/04/2016

1

An Introduction to the Data Science

Universe Mark Lee – Insight Risk Consulting mark.lee@insightriskconsulting.co.uk

07 April 2016

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The Data Science Universe

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Data Mining

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The Data Science Universe

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Colour palette for PowerPoint presentations

Dark blue

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Primary colour palette

Light grey

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Pea green

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Forest green

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Machine Learning

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Supervised Learning Data with a dependent variable/classification

Prediction

New

Data

Training

Data

Learning

Algorithm Model

Unsupervised Learning Data exploration

Data

Algorithm

New

Representation

A B

C

Model

Cat

Dog

??

Cat

Cat Dog White

Pet Black

Pet

Brown

Pet

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Colour palette for PowerPoint presentations

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Primary colour palette

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Comparing Algorithms

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Linear

models

Neural

Networks

Single

Trees

Support

Vector

Machines

K-N

ea

rest

Neig

hbo

urs

High Low Interpretability

Inflexib

le

Fle

xib

le

Transparent Black box

GLMs

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Bias-Variance Trade-off

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Trade-off

Model Complexity

Err

or

Random noise

underlying data.

Whether assumptions

of model fit the

underlying process.

Variability if the model

is trained on different

samples of data.

Combat by adding

model complexity. Combat by reducing

model complexity.

(Process Error) (Model Error) (Parameter Error)

Irreducible

Error = + + Error Bias2 Variance

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R17 G179 B162

Cyan

R0 G156 B200

Light blue

R124 G179 B225

Violet

R128 G118 B207

Purple

R143 G70 B147

Fuscia

R233 G69 B140

Red

R200 G30 B69

Orange

R238 G116 29

Dark grey

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Machine Learning

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Supervised Learning Data with a dependent variable/classification

Prediction

New

Data

Training

Data

Learning

Algorithm

Unsupervised Learning Data exploration

Data

Algorithm

New

Representation

A B

C

Model

Hyper-

parameters

Hyper-

parameters

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So which algorithm is best?

• All algorithms are biased in some manner.

• The “No Free Lunch Theorem”: no algorithm is best over all

problems.

• Methods may work well for a certain class of problems.

• Best result may be an aggregate of methods.

• In other words, “it depends”. Human judgement is required.

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Colour palette for PowerPoint presentations

Dark blue

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Gold

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Mid blue

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Secondary colour palette

Primary colour palette

Light grey

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Pea green

R121 G163 B42

Forest green

R0 G132 B82

Bottle green

R17 G179 B162

Cyan

R0 G156 B200

Light blue

R124 G179 B225

Violet

R128 G118 B207

Purple

R143 G70 B147

Fuscia

R233 G69 B140

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Issues for insurers

• New data sources – implications for better models:

– Telematics for motor insurance.

– Fraud detection via social media – several examples to date, but can it be

automated?

– Some health insurers are pricing based on health related data.

• More powerful algorithms:

– Can GLM’s keep up? Can new algorithms give a competitive advantage?

• How could London Market insurers gain from these techniques?

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Colour palette for PowerPoint presentations

Dark blue

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Secondary colour palette

Primary colour palette

Light grey

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Pea green

R121 G163 B42

Forest green

R0 G132 B82

Bottle green

R17 G179 B162

Cyan

R0 G156 B200

Light blue

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Issues for actuaries

• Will actuaries be replaced by machines/computer scientists?

• Human judgement is required - technically and statistically trained

actuaries are well placed.

• Do actuaries need to train?

– E.g., CAS Institute accreditation in data science.

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07/04/2016

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Colour palette for PowerPoint presentations

Dark blue

R17 G52 B88

Gold

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Mid blue

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Secondary colour palette

Primary colour palette

Light grey

R63 G69 B72

Pea green

R121 G163 B42

Forest green

R0 G132 B82

Bottle green

R17 G179 B162

Cyan

R0 G156 B200

Light blue

R124 G179 B225

Violet

R128 G118 B207

Purple

R143 G70 B147

Fuscia

R233 G69 B140

Red

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Orange

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References

• www.insightriskconsulting.co.uk

– We mainly use R and Python platforms.

• No free lunch theorem:

– D. Wolpert, Neural Computation 8, p1341 (1996).

• Machine learning algorithms, book and video lectures:

– Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani.

www.statlearning.com

• LinkedIn group – search for “Data science for insurers group” on LinkedIn.com.

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