1. WCI - iNED - MAID Modelling, Analytics and Insights from Data
Institute and Faculty of Actuaries | 2017

2. 4. 1. | THE LANDSCAPE 2. | MAID 3. | 4 CASE STUDIES
MAID – Overview | table of contents 1.
| THE LANDSCAPE 2.
| MAID 3.
| 4 CASE STUDIES 4.
| QUESTIONS AN iNED MIGHT ASK

3. 4. 1. | THE LANDSCAPE 2. | MAID 3. | 4 CASE STUDIES
MAID – OVERVIEW | table of contents 1.
| THE LANDSCAPE 2.
| MAID 3.
| 4 CASE STUDIES 4.
| QUESTIONS AN iNED MIGHT ASK

4. Computer Science Own Data Data Manipulation - Cleansing - Deduping
3rd Party Data
Real time
Own Data
Data Manipulation
- Cleansing
- Deduping
- Protecting
Data Sources
Social Media
Historic

5. Imagination & Visualisation
Associations & Classifications
Understanding
What does this mean
Visualisation
Correlations

6. Problem Types Decisions Clustering Time series predictions Identifying
Explanatory
Variables
GLM
Optimisation Logistics
Pattern recognition
(including text)
Modelling
Likely outcomes
probabilities
Cognitive reasoning

7. Proprietary Algorithms
Methods – Maths & Neuroscience Model assessment, selections, bias : train, test, validate
Supervised
GLMs, GAMS
Regression
Trees,
Algorithms
Nearest neighbour
NLP
(text)
Bayesian
Boosting
Machine learning
Prototypes
Association
Principal
Components
Neural nets
Boot strapping
Penalised Regression
K clustering
Random forests
Google page rank
Proprietary Algorithms
Deep learning
Undirected graphics
Unsupervised
Transparent
Black box

8. The Future Wearables (virtual reality) Algorithms AI Automation
Personalities
Robotics
Cognitive

9. Time series predictions
The Landscape
Problem types
Clustering
Pattern recognition
(including text)
Decisions
Time series predictions
GLM
Modelling
Likely outcomes
probabilities
Optimisation Logistics
Virtual
Identifying
Explanatory
Variables
Identifying
Explanatory
Variables
Physical
Computer Science
Maths & OR

10. 4. 1. | THE LANDSCAPE 2. | MAID 3. | 4 CASE STUDIES
MAID – OVERVIEW | table of contents 1.
| THE LANDSCAPE 2.
| MAID 3.
| 4 CASE STUDIES 4.
| QUESTIONS AN iNED MIGHT ASK

11. MAID – OVERVIEW | FOUR iNED
Research
Literature Reviews and Glossary of methods
Member research
Current problems
General, Life, Health, Pensions and Asset Management
Written up case studies
Future possibilities
Technology will allow?
New areas
Natural extensions?
Professional considerations
Impact on qualification, CPD, regulation of the profession, international aspects, PR/profile
Being joined up
Collaboration
Communication and awareness

12. SCOPE OF PROBLEM SOLVING
strategic positioning | current and desired situation
ANALYSIS
SCOPE OF PROBLEM SOLVING
Narrow
Broad
DOMAIN
FOCUSED
BROAD
AMBITIOUS
Aim to be known for specifics
WELL RESPECTED
Extra value-added services in many segments
REGULATORY
Known as regulatory or compliance experts
VALUE ADDING
Current aspiration
TODAY
TOMORROW
Entities should seek to avoid being in the middle
Solvency II and other pressures forcing the profession into a regulatory box
Domain expertise + Mathematical ability count at present
Data scientists a clear threat, but lack professional discipline an specific domain expertise

13. 4. 1. | THE LANDSCAPE, and MAID 2. | TYPICAL PROBLEMS & METHODS 3.
MAID - OVERVIEW | table of contents 1.
| THE LANDSCAPE, and MAID 2.
| TYPICAL PROBLEMS & METHODS 3.
| 4 CASE STUDIES 4.
| QUESTIONS AN iNED MIGHT ASK

14. Plenty scope to innovate
CASE STUDY 1 | MARINe HULL UNDERWRITING
NEW REGRESSION
Highly
traditional
Plenty scope to innovate

15. We all know this Intelligent gap fill
CASE STUDY 2 | EXPOSURE MANAGEMENT & MISSING FIELDS
PATTERN RECOGNITION
We all know this
Intelligent gap fill

16. Core actuarial Never right – but….!
CASE STUDY 3 | LIFE MORTALITY
EXTERNAL DATA SOURCES
Core actuarial
Never right – but….!

17. TIME SERIES STRATEGIC TACTICAL
CASE STUDY 4 | ASSET ALLOCATION
TIME SERIES
STRATEGIC
TACTICAL

18. 4. 1. | THE LANDSCAPE 2. | MAID 3. | 4 CASE STUDIES
MAID – OVERVIEW | table of contents 1.
| THE LANDSCAPE 2.
| MAID 3.
| 4 CASE STUDIES 4.
| QUESTIONS AN iNED MIGHT ASK

19. MAID – OVERVIEW | Questions for an insurance NED to ask1.
DATA – How reliable? – Governance and testing 2.
DATA SOURCES – How many? – Sniff test - probe 3.
INITIAL VIEWS – How open minded? – Unsupervised testing 4.
WHY CHOOSE MODEL? – Bias and fit - How answers vary with model selected 5.
KEY JUDGEMENTS – What assumptions? – What key judgements? 6.
COMMON SENSE – Explain in words of one syllable what’s going on 7.
ETHICS – What are the cyber/data security/ethical issues? – How have you managed them? 8.
REGULATORY – General Data Protection Regulation – Stricter consent – use of mobile apps – freedom of information 9.
THE DANGER OF AUTOMATION – Data and pattern discontinuity – Fool yourself – be the professional sceptic
10.
UNEXPECTED CORRELATIONS – Economic catastrophes – Collapse of antibiotics - terrorism