Risk Dashboards with R Shiny: a C-ROSS case study · Risk Dashboards with R Shiny: a C-ROSS case...

Risk Dashboards with R Shiny: a

C-ROSS case study

Phil Joubert, Oliver Wyman

[email protected]

06 May 2015

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Agenda

• Setting the scene:

– What is C-ROSS?

– What is a risk dashboard?

– What is R Shiny?

• Putting it together:

– Designing the system

– Designing the dashboard

– Developing code in RStudio

• The payoff:

– What’s wrong with our current set up?

– Delivering insights (or, what’s the point of all this?)

– Summary and conclusions

• Q&A

06 May 2015 2

Setting the scene

06 May 2015


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Setting the scene

2. Risk Dashboards

Trendy and useful

presentation technique

1. C-ROSS

New Chinese regulatory

framework

3. R Shiny

Trendy web-interface extension to a popular statistical

programming language


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What is C-ROSS? Overall framework of C-ROSS

Source: “Overall Framework of the Second-Generation Solvency Supervision System of China” by CIRC

Emerging market

Pillar 1 - Quantitative

capital requirements

• Quantitative capital

requirements

• Actual capital

assessment standards

& capital classifications

• Supervision measures

Pillar 3 - Market

discipline mechanism

• Improve risk disclosure

• Transparency

• Develop market

disciplinary mechanism

and optimize the market

environment

Unified supervision

Pillar 2 - Qualitative

supervision

requirements

• Integrated Risk Rating

• Solvency Aligned Risk

Management

Requirements and

Assessment

• Supervision check and

measurements

Risk-oriented and Value-based

Company’s solvency management

1

2

3

System

features

Supervision

elements

Supervision

basis


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C-ROSS: Pillar One

06 May 2015 6

Total Required

Capital

Life Non-Life

Premium &

Reserve Catastrophe

Typhoon

Earthquake

Property

Auto

…

Agricultural

Mortality &

Morbidity Surrenders Expense

Projected

Mortality

Pandemic

Mortality

…

Morbidity

Projected

Surrender

Mass

Surrender

Market Credit

Interest

Rates

Equity

…

Real Estate

Spreads

Defaults

Premium

Reserve

Premium

Reserve

Premium

Reserve


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What is a Dashboard?

06 May 2015 7

Communicates information succinctly make it simple

Delivers pertinent information but not too simple

Uses knowledge of human perception make it visual

Displays deeper information on demand drill-down capability


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(Risk) Dashboards – examples

06 May 2015 8

All

investments

/ cash in

one place

Why this

position?

Bright

colours, big

fonts

Relative

importance?

Pie charts?


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(Risk) Dashboards – examples

06 May 2015 9

Too much information

Not visual enough


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Risk type and

BU specifics

Risk type

and

BU overview

Position and exposure

drill-down

Board BU

management

level Reporting hierarchy

Report

Appendix

Executive

summary

Report

Appendix

Executive

summary

Report

Appendix

Enterprise

overview

Executive

management

level

Risk reporting hierarchy model

Managers of differing seniority have

different needs


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What is R Shiny?

• Web interface for R

• Designed to deliver small

apps

• Simple rules based layout

structures

• Built as part of the RStudio

suite

• Uses CSS / Node.js under

the hood

06 May 2015 11

http://www.davesteps.com/homebrewR/

Putting it together

06 May 2015


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Schematic of a management information

system

06 May 2015 13

Data

Parameters

Results

Report generation

/ presentation tool

Calculation

engine


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Schematic of a management information

system – demo implementation

06 May 2015 14

Data

Parameters

Results

Calculation

engine

Files R Package Memory R Shiny

Report generation

/ presentation tool


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Designing your C-ROSS calculations

06 May 2015 15

1 ρAB ρAC ρAD

1 ρBC ρBD

1 ρCD

1

Risk A Risk B Risk C Risk D

1 … … …

1 … …

Risk X = E x RF

= E x RF0( 1 + k1 … kn)

Table lookups

Aggregation = VCV

Matrix Multiplication

• Hierarchy data structure

• Node walking

• I / O

Other required features


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Designing your dashboard:

Brainstorm some ideas • Solvency margin (current, recent)

• Integrated Risk Rating

• Required capital vs Available capital

• Available capital by tier

• Required capital by risk

• Required capital by BU

• Asset mix

• Exposures by BU / geography

• Situation under stress scenarios

• Market indicators

• Narrative / action log

06 May 2015 16

1

2

3

4

5

6


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Developing code in RStudio

Issue with R alone Solution with RStudio

No IDE Visual debugger (break, watch, step over, etc)

No version control Integrated version control with git and github (and

SVN)

Difficult to check / test

code and difficult to apply

the encapsulation principle

Accessible development of packages, including

testing

06 May 2015 17

Some issues remain:

• Obscure syntax

• Run times

• Open source


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X

R Shiny – the basics

06 May 2015 18

Web

browser

Web server

ShinyApp server

UI.r Server.r Any R

code

1

2

Input

Reactivity

Y Output Y = f(X)

When user changes X, Shiny recalculates f() and updates Y


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Package shinydashboard

library(shiny)

library(shinydashboard)

dashboardPage(

dashboardHeader(title = "Old Faithful Geyser Data"),

dashboardSidebar(),

dashboardBody(

fluidRow(

box(

sliderInput("bins", "Number of bins:", min = 1, max =

50,

value = 30)

),

box(

plotOutput("distPlot")

))))

06 May 2015 19

UI.r

Load up the new library

Slightly different to the

standard Shiny

declarations

Every element needs to

be in a box (unless it is

already a box!)


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Package shinydashboard

06 May 2015 20

Look familiar?


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Implementing your C-ROSS dashboard

dashboardPage(

dashboardHeader( … ),

dashboardSidebar( … ),

dashboardBody( … )

)

06 May 2015 21

Title

List tabs

Lay out each tab’s

contents

A

B

C

A

B C


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tabItems(

tabItem( tabName = "Summary",

fluidRow(

valueBoxOutput( "vbxSummaryCalculationDate" ),

valueBoxOutput( "vbxSummarySolvenyMargin" )

))

06 May 2015 22

output$vbxSummarySolvenyMargin <- renderValueBox({

valueBox( printPercent( LatestSolvencyMargin ),

"Solvency Margin",

icon= icon("money"),

color = ragColour(LatestSolvencyMargin) )

})

UI.r

Server.r

A

A

B

B

C

C


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Available and Required Capital

06 May 2015 23

Drill down


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Exposures by BU

06 May 2015 24

The payoff

06 May 2015


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What’s wrong with our current set-up?

1. Out-dated techniques

2. Bad engineering: failing to minimise operational risks

and optimise rewarded risks

3. Poor use of resources

06 May 2015 26


presentations

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Delivering insights (or, what’s the point?)

To make good business decisions managers need information which is:

• presented clearly

• accurate

• timely

• A well designed dashboard will address the presentational issue

• Accuracy is improved by using code which is tested and version controlled

• Timeliness is a function of people and process – the people issue is often

easier to optimise than process (e.g. data delivery times may be outside of

your control)

06 May 2015 27


presentations

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Summary and conclusions

C-ROSS A quantum leap for the Chinese insurance industry,

which should improve management and customer

outcomes, but which presents short-term

operational, systems and education challenges

Dashboards A neat way of presenting pertinent information

visually, and an opportunity for actuaries and

insurance risk people to take advantage of insights

developed in the software industry

R Shiny A quick, powerful way of creating and delivering

management information, and a welcome user

interface to R

06 May 2015 28


presentations

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06 May 2015 29

Expressions of individual views by members of the Institute and

Faculty of Actuaries and its staff are encouraged.

The views expressed in this presentation are those of the

presenter.

Questions

?

Comments

?


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Speaker Bio

06 May 2015 30

Phil Joubert is a principal in the Financial Services practice of Oliver Wyman, based in Hong

Kong Office. Phil has fifteen years of experience in the financial services industry, having worked

in areas as diverse as actuarial consulting and derivatives trading. He has worked in both Europe

and Asia-Pac for a variety of insurers, banks and software houses, and specialises in risk and

capital modelling and systems design

Recent experience

• Regulatory capital model design and implementation for several insurers in Europe

• Economic capital implementation for leading pan-Asian insurance group

• Derivatives trading and market risk management

• Capital aggregation systems design for leading ESG provider

• Actuarial automation implementation project for specialist life insurer

Phil holds an MSc in Finance & Mathematics from Imperial College and is a Fellow of the Faculty

of Actuaries. He joined Oliver Wyman in 2014, having spent several years as an independent

actuarial consultant. Previously he worked at Deutsche Bank and Natixis as a trader and he

started his career with Deloitte Actuarial.

Risk Dashboards with R Shiny: a C-ROSS case study · Risk Dashboards with R Shiny: a C-ROSS case...

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