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Machine learning – making an impact

Tony Bradshaw, Duncan Anderson

19 April 2018

PC European Actuarial Directors’ Forum

© 2018 Willis Towers Watson. All rights reserved.

willistowerswatson.com 2© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

Agenda

Machine Learning - Making an impact - Wider implications

Tony

Machine Learning - Why now? What’s involved? Does it work? Implications for actuaries?

Duncan

This is not all new

3

1940s Today1960s 1980s 2000s

Trees

GLMs

Actuaries adopt GLMs

1950s 1970s 1990s 2010s

First computer

Neural nets

CART

GBMs

Random forests


https://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwi1homunrLaAhXM6xQKHV9ZC84QjRx6BAgAEAU&url=https://askleo.com/why_wont_my_program_use_more_than_25_of_the_cpu/&psig=AOvVaw1MD8p22r4_7QhNcEmRqaF4&ust=1523536616130119


This is not all new

4

1940s Today1960s 1980s 2000s

Trees

GLMs

Actuaries adopt GLMs

1950s 1970s 1990s 2010s

First computer

Neural nets

CART

GBMs

Random forests


Integrated

environments

and services

Hyper scale

parallel

computing

Distributed

storage/

processing

with Hadoop

NoSQL

databases

Data

visualisation

tools

Free software

environments,

languages,

analytics

libraries

Data stream

and real-time

processing

supporting IoT



There is a spectrum of complexity

5

Harder

Evolving

Requires significant expertise

Not at all hard

Already in use

Existing teams can normally do this stuff

AI comprehension Bespoke image recognition Speech analytics Machine learning predive modelling

Full autonomous driving Object recognition Topic modelling Automated GLMs

“Vastly more

risky than

North Korea”

GLM

stepwise

macro


Example machine learning methods

6

Ensemble

Methods

Classifications

Trees"Earth"

Support Vector

MachinesElastic Net Neural Networks

Gradient

Boosting

Machines

Naïve Bayes

Regression

Trees

Principal

Components

Analysis

LassoK-nearest

Neighbours

K-Means

Clustering

Random Forests

Ridge

Regression


Fitting - Penalised Regression

© 2018 Willis Towers Watson. All rights reserved.on client use only. 7

𝐿(𝛽|𝑋, 𝑦) + 𝜆1 𝑖𝛽𝑖 + 𝜆2

𝑖𝛽𝑖2

f(x) = g-1(X.b) where b estimated by minimising

Elastic Net

Ridge Lasso GLM

Fit

Fit

Test

1

Holdout

Fit

Fit

Fit

Fit

Fit

2

Holdout

Fit

Test

Fit

Test

Fit

3

Holdout

Fit

Fit

Test

Fit

Fit

4

Holdout

Fit

Fit

Fit

Fit

Fit

5

Holdout

Test

Fit

1

0.9

5 0.9 0.8

5 0.8 0.7

5 0.7 0.6

5 0.6

0.5

5 0.5

a

Cro

ss-v

ali

da

tio

n e

rro

rlog(l)

Optimal parameters

𝜆1 = 𝜆𝛼, 𝜆2 = 𝜆1 − 𝛼

2

𝐿(𝛽|𝑋, 𝑦)

Fitting - Gradient Boosted Machine or “GBM”

8

Group < 15?

Age < 40?

All dataGroup < 5?

Y N

Y N

Y N

𝑓 𝑥 = λ 𝑛=1

𝑁

𝑓𝑛(𝑥)

λ + λ + λ + λ +

λ + λ + λ + λ +

λ + λ + λ + λ +

λ + λ + λ + λ


𝑓 𝑥 = λ 𝑛=1

𝑁

𝑓𝑛(𝑥)

λ + λ + λ + λ +

λ + λ + λ + λ +

λ + λ + λ + λ +

λ + λ + λ + λ

Optimal model of

those considered

Fitting - Gradient Boosted Machine or “GBM”

9© 2018 Willis Towers Watson. All rights reserved.

l(learning rate or “shrinkage”)

Interaction depth(number of splits on each tree)

N(number of trees)

Bag fraction(proportion of data used at each iteration)

y

w3,3

w3,2

w3,1

x1=21

x2=5

w1,1,3

α1,3w1,2,3

w1,2,2

α1,2

w1,1,2w2,1,3

w2,3,1

α2,3

𝛼2,2

𝛼2,1

w2,3,3

w2,3,2

w2,1,2

w2,1,1

w2,2,3

w2,2,1

w2,2,2

𝛼1,1w1,1,1

w1,2,1

Fitting - Neural Networks


Do they work? Do they add value?

11

Ensemble

Methods

Classifications

Trees"Earth"

Support Vector

MachinesElastic Net Neural Networks

Gradient

Boosting

Machines

Naïve Bayes

Regression

Trees

Principal

Components

Analysis

LassoK-nearest

Neighbours

K-Means

Clustering

Random Forests

Ridge

Regression


Dimensions of utility

12

Analytical

time and

effort

Predictive power

Execution speed Implementation

Interpretation

Method

Stability



13

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


Data

Factor

engineering &

knowing what

to model Method


14

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


Think of a model…

Multiply it by 123

Square it

Add 74½ billion

…and you get the

same Gini coefficient!


15

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


Model RMS Error

GLM 34.7%

Neural Net 33.1%

GBM 31.0%


16

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability

Loss ratio improvement 3.1%!© 2018 Willis Towers Watson. All rights reserved.


17

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability



19

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


Pre/post

adjustments

“Comfort

Diagnostics”

Model

0

20000

40000

60000

80000

100000

4.4%

4.9%

5.4%

5.9%

6.4%

< 88% 88% -90%

90% -92%

92% -94%

94% -96%

96% -98%

98% -100%

100% -102%

102% -104%

104% -106%

106% -108%

108% -110%

110% -112%

> 112%

Resp

on

se

Proposed Model / Current ModelObserved Current Model Proposed Model

“Comfort diagnostics”

Dimensions of utilityHow much do you need to understand?

How much would you normally understand? (eg vehicle classification)

Cost of error? (eg marketing, operational efficiency)

Regulatory requirements

Professional standards

20

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability



21

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability

Main Policy

Admin System

Next gen

rating

engine

Analytical

environ-

ment

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

770

780

790

800

New Business Price £

Cu

rren

t R

ate

-2.0% +2.0%

-5.0%

+5.0%

10%

40%

40%

10%

Analytical

environment

Next generation

rating engine

Policy administration

system



22

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


A toolkit…

23

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability

Analytical

time and

effort

Predictive power

Table

Implementation

Interpretation

Penalised

Regression

Stability

Execution speed

Analytical

time and

effort

Predictive power

Execution speedTable

Implementation

Interpretation

"Earth"

Stability

Analytical

time and

effort

Predictive power


Implementation

Interpretation

GBMs

Stability

Analytical

time and

effort

Predictive power


Implementation

Interpretation

Trees

Stability

Analytical

time and

effort

Predictive power


Implementation

Interpretation

Random

Forests

Stability

Analytical

time and

effort

Predictive power


Implementation

Interpretation

Support

Vector

Machine

Stability

Analytical

time and

effort


Implementation

Interpretation

Neural

Networks

Stability

Predictive power

Analytical

time and

effort

Predictive power


Implementation

Interpretation

GLM

Stability


That is already in use…

24

Analytical

time and

effort

Predictive power


Interpretation

Method

Stability


2017 Willis Towers Watson US market survey

For which business applications do you use or plan to use these methods?

Willis Towers Watson Predictive Modeling Survey 2017

Underwriting

and risk

management

Pricing ReservingClaims

management

Customer

management

Marketing

and

Distribution

ClaimsUnderwriting/Pricing Marketing

94%

84%

55%

41%

37%

41%

41%

37%

37%

20%

Generalized linear models(GLMs)

One-way analyses

Decision trees

Model combining methods(e.g., stacking, blending)

Gradient boosting machines(GBMs)

Random forest (RF)

Penalized regressionmethods (e.g., lasso, ridge,

elastic net)

Neural networks

Generalized additivemodels (GAMs)

Support vector machines

61%

58%

58%

27%

24%

36%

27%

24%

21%

12%

78%

54%

54%

35%

32%

35%

30%

41%

19%

19%

That spectrum of complexity

25

Becoming BAU

This end could be

interesting…

AI comprehension Bespoke image recognition Speech analytics Machine learning predive modelling

Full autonomous driving Object recognition Topic modelling Automated GLMs


So…


It’s not just about methods

Data beats models

It’s not just about methods

Working out what to model matters -

Data beats factor engineering beats models

Machine learning is already in use

Actuaries are already involved

It’s not just about predictiveness

A broader set of problems can be analysed

- operationalising rapid insight

Evolution not revolution

Models are complementary

to existing methods

PCEADF

Issues for the Profession(s)


Regulatory issues

TAS: Judgement - what judgement?

GDPR

UK Government Select Committee

(Science and Technology)

Training

A generation less familiar with stats?

CAS, SOA ahead? (eg CSPA)

IFoA on the case, but fast enough?Role of the actuary

Domain expertise matters (at least currently)

Easier for an actuary to pick up machine learning

than for a data scientist to understand insurance?

Siloed teams don’t work

Familiarity and the right vernacular can help

Scope of involvement?

Pricing Reserving Claims analytics

Customer management ? Marketing ???

Machine learning making an impact - Willis Towers … › assets › 23679 › D1-S3...Machine...

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