Big Data in Actuarial Space –How Big is Enough?

By Haofeng Yu

Actuaries’ Club of the Southwest

Fall Meeting 2017

Agenda

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

2

Prelude…

"Toto, I've a feeling we're not in Kansas anymore.”

Dorothy, The Wizard of Oz

4

I Our Actuarial SpacePast paradigm shifts:

• Refined UW with Preferred classes• Rise of Term Life• Rise of Variable Products• Rise of DC plan• Obama Care• ERISA/RBC(C3Px)/ERM/PBA• Living To 100• Business Intelligence• Big Data

Best advice going to SOA Annual Meetings

5

I

“Go early, sit close.”

SOA AME

Opening General Session Speakers

KeywordsPresidential

Lunch SpeakersKeywords

2013 Dan RoamThe Back of the Napkin: Solving Problems and Selling Ideas with Pictures

Zanny Minton-Beddoes

economic editor for The Economist

2014 Adam Steltzner

Lead Landing Engineer of NASA's Mars Science Laboratory Curiosity Rover Project

Madeleine Albright

Former U.S. Secretary of State

2015 Salim Ismailtechnological developments and its implications on society.

Shawn AchorHappiness, Success, Positive Psychology

2016 Sal KhanFounder Khan Academy, Education innovator

Nick BiltonTechnology Journalist and Author, hardware hacker

2017 Kenneth Cukier

coauthor of Big Data: A Revolution That Transforms How We Live, Work, and Think

Scott PageProfessor, teaches popular course, Model Thinking, on Coursera

Is this a hype?

6

I

Is this a hype?SOA AME

Opening General Session Speakers

KeywordsPresidential

Lunch SpeakersKeywords

2013 Dan RoamThe Back of the Napkin: Solving Problems and Selling Ideas with Pictures

Zanny Minton-Beddoes

economic editor for The Economist

2014 Adam Steltzner

Lead Landing Engineer of NASA's Mars Science Laboratory Curiosity Rover Project

Madeleine Albright

Former U.S. Secretary of State

2015 Salim Ismailtechnologicaldevelopments and its implications on society.

Shawn AchorHappiness, Success, Positive Psychology

2016 Sal KhanFounder Khan Academy, Education innovator

Nick BiltonTechnology Journalist and Author, hardware hacker

2017 Kenneth Cukier

coauthor of Big Data: A Revolution That Transforms How We Live, Work, and Think

Scott PageProfessor, teaches popular course, Model Thinking, on Coursera

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

7

What is an Elephant or Big Data?

8

I

The parable of blind men feeling elephant tells us:• you could be locally right,• yet globally wrong,• depending on your perspectives.

Big Data often associates with:data warehouse, data lake, date ocean, cloud

computing, Cognitive computing deep learning, supervised learning, unsupervised learning, Hadoop, tensor flow, IoT, wearables, and/or crowd-sourcing, statically learning, machine learning, AI, Blockchain,

FinTech, InsurTech, business intelligence, etc. predictive analytics

We are drowning in information and starving for knowledge — Rutherford D. Roger, 1985

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

9

Insurers’ perspective

10

II

Big data

FinTech/InsurTech disruptors

FinTechNow describes a broad variety of technological interventions into personal and commercial finance

Keywords:

Blockchain, Crowd-sourcing, peer-to-peer

Disruptors:

LendingClub, WealthSimple, Finn.ai, OutsideIQ

InsurTechRefers to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model

Keywords:

on-demand, peer-to-peer, wearables, micro events

virtual distribution

Disruptors:

Ladder, Haven, Besure, Tuque, Energlm Sonnet, Trov, Zensurance, Lemonade

FinTech/InsurTec definitions by Investopedia 11

II-c

Enabling solutionsBig data techniques, cognitive computing (e.g., AI), cyber/mobile technology, etc.

Insurers’ perspective

12

II-e

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

13

Big data less hype for actuaries

14

III

a. Data

b. Techniques

c. Big data for insurance value chain

DataSet, group, base, warehouse, lake, ocean, cloud, and universe

Happy Thanksgiving!

III-a

All data?

Google Flu Trend

16

III-a

Events and choices in a life cycle

Graph source: Agent Based Modeling and Its Actuarial Applications, Lombardi and Rao, PWC SOA Webcast, 2015.

Zero data?

AlphaGo Lee was trained using 30 millions of human moves; AlphaGo Zero none!

Go is a game with perfect information in a closed system,

but Insurance industry is an open system, not well defined, and full of Imperfect information and behavior

The Game of the 20th Century: Go Seigen (Black) vs. Honinbo (White), 1933 17

III-a

Data - the art of the possible

Source: McKinsey & Company, presented in Transforming Life Insurance with Design Thinking, by way of RGA

III-c

Apply big data, incrementally, while be aware of regulatory/legal boundaries

Techniques

Graph by RGA, Peter Banthrope, SOA PA Symposium 2017 19

III-b

Supervised learning Regression

Classification

Unsupervised learning Clustering

Others: Dimension Reduction

Genetic Algorithm

Reinforcement learning

Deep Learning

Be practical! Start with

Big data for Insurance value chain

20

III-d

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

21

Process of Predictive Analytics

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IV

1 Define the Problem"Far better an approximate answer to the right question ... than the exact answer to the wrong question..." - John Tukey

2 Data Collection What could be used? What should be used?

3 Data Preparation Data structure and scrub; variable transformation and grouping

4 Build Model Select, fit and diagnose, etc.

5 Model Validation Parameterize model on the test dataset, k-fold cross-validations

6 Model Evaluation out-of-time test, Gini

7 ImplementationEasy to solve a problem, hard to prove a theory?Easy to solve theoretically, hard to operationalize a solution!

Ewald and Wang, Predictive Modeling: A Modeler’s Introspection, 2015, SOA

I. What is Big Data?

II. Insurers’ perspective

III. Big data less hype for actuaries

IV. Process of Big Data or Predictive Analytics

V. Applications with a mini lab on Jupyter

23

Set up a mini PA lab

24

V

a. Excel

b. SQL type of Environment

c. R + RStudio

d. Python + Anaconda

Welcome aboard! Let us go to Jupyter!

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V Examples on JupyterWith potential applications for Actuaries

Platform Examples Applications for Actuaries

Supervised learning

R Regression GLM for Assumptions setting

R Classification Logit Regression for UW

Unsupervised learning

R Clustering Risk segmentation

Python Agent based model Policyholder behavior simulation

Others

Reinforced learning UW

Deep learning Distribution model

Dimension Reduction

Model Ensemble

…

R, Python,

GPU,

Hadoop,

cloud,

TensorFlow,

etc.

Jup

yte

r

Final Thoughts

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a. Embrace big data, incrementally

b. Take action - set up your Big Data lab and explore

c. Merely an actuary? Be a PAFer? https://www.soa.org/predictive-analytics-and-futurism

Be a Kaggler? https://www.kaggle.com/50119-Kaggler

Additional References

1. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, 2013 http://www-bcf.usc.edu/~gareth/ISL/book.html,

2. Peter Harrington, Machine Learning in Action, 2012 (using Python)

3. Alan Mills, “Complexity Science - An introduction (and invitation) for actuaries”, SOA Special Report, 2010

4. Piet de Jong and Gillian Z. Heller, Generalized Linear Models for Insurance Data, 2008

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Questions?

Limitations - The views expressed in this presentation are those of the presenter, and not those of AIG. Nothing in this presentation is intended to represent a professional opinion.

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Appendix - Agent Based Modeling

Traditional actuarial models largely project historical aggregate patterns and “actuarial judgment” into the future - top-down.

ABM simulates agents’ (e.g., policyholders, agents and brokers) interactions with their environment and other agents in order to understand the emergent behavior of complex systems – bottom-up

ABM helps understand

• Strategy & Growth

• Marketing & Sale

• Product Development & UW

• Process & Operation

• Inforce Management

• Capital, Risk & Finance

30Graph source: “Agent Based Modeling and Its Actuarial Applications”, Lombardi and Rao, PWC, SOA Webcast, 2015.

Appendix - Agent Based Behavioral Simulation