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

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. | 10 QUESTIONS AN iNED MIGHT ASK

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. | 10 QUESTIONS AN iNED MIGHT ASK

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

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

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

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

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

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

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. | 10 QUESTIONS AN iNED MIGHT ASK

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

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

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. | 10 QUESTIONS AN iNED MIGHT ASK

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

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

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

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

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. | 10 QUESTIONS AN iNED MIGHT ASK

MAID – OVERVIEW | Questions for an insurance NED to ask 1. 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