Session 058PD: Artificial Intelligence for Actuaries

10/15/2018

3:30-4:45 p.m.

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Artificial Intelligence for ActuariesModerator: Sarah Abigail

Presenters: Shankar Vaidyanathan

Martin Snow, FSA, MAAA

Gaurav Gupta

October 15, 2018

#AIforActuaries

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Sarah Abigail

Co-Founder Ironbound Consulting Group

@ironbcg

Shankar Vaidyanathan

Founder and CEONoonum

@_noonum_

Martin Snow, FSA, MAAA

VP, Chief Delivery OfficerAtidot

@AtidotIsrael

Gaurav Gupta

Founder and CEOQuaEra Insights

@QuaEraInsights

Artificial Intelligence for Actuaries

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Artificial Intelligence & Machine Learning PAST, PRESENT, AND FUTURE

Shankar VaidyanathanFounder & CEO, shankarv@noonum.comhttp://linkedin.com/company/noonum@_noonum_

http://linkedin.com/company/noonum

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What is Artificial Intelligence?Human intelligence "can be so precisely described that a machine can be made to simulate it".

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Intelligence

• Learning – rote learning and generalization• Reasoning – inductive and deductive• Problem solving – special purpose or general

purpose

• Language – Text, Speech, Images, Video• Perception

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Origin

• Alan Turing• Idea of machine intelligence• Cryptanalyst during World War II

• Workshop at Dartmouth College in 1956• Allen Newell (CMU)• Herbert Simon (CMU)• John McCarthy (MIT)• Marvin Minsky (MIT)• Arthur Samuel (IBM)

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Chess

• Drosophila of AI• Methods of problem solving and learning tested• Use heuristics to narrow down moves• 1997 – computer beat the world champion• Still a lot of computing involved

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Self Driving Car

• 2 million miles of driving experience• Constantly processing data from

sensors

• Learn to respond to risky situations effectively

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Companyof

Interest

Customers

Competitors & Disruptors

Fundamentals

Economic

Sentiment

Suppliers & Partners

understands companies

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Market (Stock/Bond)

CompanyFundamentals

Economic

Social & News Media

Analysts reports

Alternative

Other (Weather,

Health)

BYOD

insights

is constantly learning…

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Other Applications

Finance

InvestmentsCorporate Strategy

BankingActuarial

CryptocurrenciesBlockchain

Health Care

Hospital readmissionPatient Care

Improvement

Industrial

Internet of ThingsFailure Prediction

Consumer

Smart PhonesIntelligent Homes (IoT)

…

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Growth in AI and Machine Learning

Cloud• Powerful Computing Machines• Availability of lots of Data and storage capacity• Connectivity

Public libraries

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Future Challenges

• Ethical decisions • Drawing inferences relevant to a

situation

• Connotation and words with double meanings

• Non-verbal cues• Emotions in language

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Future Advances

Improvement in Storage and Processing

Quantum Computing

Improvement in Connectivity

Better Implementations

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Thank You

Shankar VaidyanathanFounder & CEO, shankarv@noonum.comhttp://linkedin.com/company/noonum@_noonum_

http://linkedin.com/company/noonum

Emerging Uses of Predictive Analytics

SOA 2018 Annual MeetingSession 58

Martin SnowVice President & Chief Delivery Officer

Member Advisory BoardAtidot

October 15, 2018

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Predictive Analytics Data Flow

Additional TablesOne view of the client

Policy Administration Transactions

Customer RelationshipManagement

Business question

Historical / Snapshot data

Feature engineering

External data

The modeling Arena

Results & Insights Actions

Validation

Business Questions and Modeling

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Use Case: Retention

Business Question: Can we determine who is likely to lapse and why to improve retention?

Model Objective: Predict who is at risk of lapsing to foster conservation efforts.

Model Considerations:May want to fit several models – based on product complexity, variation by policy duration, data availability etc.

What the Model Does: Classifies policyholders based on propensity to lapse using supervised learning. Validation: Based on observed past experience.

Business Questions and Modeling

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Use Case: SalesBusiness Question: Is there an underinsurance opportunity?

Model Objective:Find outliers with less insurance than peers; Rank them based on propensity to buy (and need for) more insurance; Provide insights on the influencing features

Model Considerations:Unsupervised clustering of similar individuals vs. supervised regression over the amount insured; width of distribution around the mean; model overfitting

What the Model Does:Group policyholders based on similar characteristics –policy type, insurance characteristics, demographic information – using regression analysis.

Validation: ?

Feature engineering

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Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work.

Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive.

(Wikipedia)

Feature engineering - examples

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How would we incorporate the issue date of 11/22/2017 ?Feature Meaning

Date Models long term trends

November Month-specific sales features, year end targets

22nd day of the month Month end targets?

Wednesday Significance of day of the week

Proximity to public holidays Policyholder behavior

Proximity to financial / political events ….. Policyholder behavior

Premium / Contribution information: Missing / Skipped premium - How many times did a policyholder miss a premium?

- How recently?

- How to combine the information – for example:Missing Premium = (number of times missed in last 24 months) * (1 / distance of last one)

Feature engineering - examples

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“Free-Text” professions:

- Thousands of different occupations, not useful for analysis

- Used advanced clustering techniques to map to 10 groups

- Result: Occupation is significant for propensity to lapse

- Another enhancement – connect to underwriting risk classification

External Data

• Most common – Demographic data based on address, for example: Median earnings, average household, homeowner vacancy, median age

• Lifestyle type data (subscriptions, etc.)

• Depending on product, market data can also be useful

• Life cycle events, from external sources, can be useful triggers, for example:

• house purchase

• Job change

• New family member

• Company data which is “external” to the block analyzed – for example from other operations (health, P&C?)

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All data and (engineered) features can be useful and may lead to powerful insights –

Time spent here is usually well rewarded!

Validation (lapse example)

• How do we know the model is “working”?

• Back-testing against historical data and against company assumptions:

• Define “training” period (eg 2010-2015)

• Defined “back testing” period (eg 2016)

• Check actual and own company assumptions against model results for 2016

• Continuous monitoring throughout

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Insights – Example - Persistency by features

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Pensioner Clerk Housewife Teacher Manager

• Once model is validated, we get three useful outcomes:

• Feature importance

• Predictions on a per policy level

• Ability to predict target based on simulated input

• Features can be grouped into two categories:

• Indirect (e.g., occupation)

• Direct (i.e., company can influence)

• External (e.g., market interest rates)

Insights – Example - Persistency by features

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Insights into actions and simulations

• For a lapse / underinsurance model, results are probabilities per policy. The model can assist in prioritizing policies for conservation or up-sell:

• Either when the client approaches the company

• Or proactively

• By adding an additional layer of per-policy profitability, insights turn from inforce projections to value projections

• In addition, model can be used to simulate business under various scenarios.

• Optimization techniques can be applied to select the preferred outcome

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Fixed Annuities and Reinsurance

• Low interest rate environment leads to spread compression and lower profit margins

• Companies are not prepared to invest more capital

• De-risk the balance sheet, diversify the business and improve investment margins

• How well does the cedant understand its lapse experience?

• How well does the reinsurer understand its lapse experience?

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Fixed Annuities Lapse Experience

• What data do you have?

• Do you have the skills to use machine learning and predictive analytics on this data?

• Do you have data scientists with expertise in the annuity business?

• What type of data have your machines been trained on?

• What type of turn around do you want from predictive analytics?

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Some closing thoughts

• Predictive analytics can be a powerful tool in managing in force business and generating new sales

• Results depend on availability of data. Frequency of client contact impact results. Use of external event data can substitute for missing internal data, but is more difficult / expensive to obtain.

• Feature engineering and augmentation are critical.

• We have not discussed feature correlation and masking but these are important issues which are tricky to handle

• Additional layers (e.g., profit) can be incorporated for simulations to help with strategic decisions

• Best results are achieved when predictive analytics are integrated to the business process

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Artificial Intelligence for ActuariesModerator: Sarah Abigail

Presenters: Shankar Vaidyanathan

Martin Snow, FSA, MAAA

Gaurav Gupta

October 15, 2018

#AIforActuaries

2

Sarah Abigail

Co-Founder Ironbound Consulting Group

@ironbcg

Shankar Vaidyanathan

Founder and CEONoonum

@_noonum_

Martin Snow, FSA, MAAA

VP, Chief Delivery OfficerAtidot

@AtidotIsrael

Gaurav Gupta

Founder and CEOQuaEra Insights

@QuaEraInsights

Artificial Intelligence for Actuaries

3

Artificial Intelligence for Actuaries

Session 058PD

SOA Annual Meeting, 2018

Gaurav GuptaFounder & CEO

ggupta@quaerainsights.com

How Can YOU Use it?

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Powering Life Insurance with AI techniques

Supervised learning

Deep learning

Reinforcement learning

NLP

Bayesian

Regression

ClusteringDimensionality

reductionSVM

GAMRNN

Simulation

Recommendation systemRandom forest

Decision trees

GLM

Monte Carlo

AI

How to build the bridge?

Retention

Life Insurance

Cross-sell

Pricing

Reserving

Product development

Anti-selection

Distribution

Assumption setting

Mortality study

Reinsurance

Gradient boosting machine

Emerging experience

Risk management

Premium persistency

Underwriting

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PRICING | UNDERWRITING | RETENTIONPRODUCT DEVELOPMENT | RESERVING

CROSS-SELL | PREMIUM PERSISTENCYMORTALITY STUDY | ANTI-SELECTION

RISK MANAGEMENT | ASSUMPTION SETTING

Where is AI being used in Life Insurance?

AI + P&C

RETENTION

PRICING

UNDERWRITING

PRODUCTDEVELOPMENT

LAPSE

RESERVING

CROSS-SELL

PERSISTENCY

MORTALITY STUDYANTI-SELECTION

EXPERIENCE STUDYASSUMPTION SETTING

AI + LIFE INSURANCE

Where is AI being used in Life Insurance?

* Data collected from Google scholar search, number of articles10

RISK MANAGEMENT

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What if you could…

… Monitor mortality constantly

… Add and analyze unlimited data, new variables quickly

… Predict policyholder behavior (shock lapse, anti-selection, …)

… Develop new mortality assumption in half an hour

… Price at an individual level

… Develop customized products on-the-fly

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Why AI has not been fully exploited by life actuaries

• Hurdles for AI to fit existing paradigm

• Talent to understand both AI and life insurance is scarce

• Difficulties in changing business processes and systems

• Lengthy validation

• Death is a rare event, and has a long duration – low credibility

• Inconsistency of data dimensions, variable definitions etc. across systems, over time

• Blackbox is a no-no• Regulation

Regulatory Data Other…

Case Study:Experience Study

Identify segments of mortality deviation Integrate underwriting, transaction, claim, external data in real-time Discover key drivers for mortality/lapse deviation Detect early alerts for better/worse mortality trends Generate reports in minutes instead of weeks

AI can help you do things faster and easier

Example Areas Experience studies Automate reporting

How AI Achieves it More powerful data handling - 1) more data sources; 2) easier data manipulation; 3) faster Easier to develop and monitor KPIs (e.g. deviations, trends) Large selection of algorithms, software and platforms

Identify segments of mortality deviation

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~100%

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Actuals to Expected (A/E) ~500,000 exposures

>120%

Case Study:Assumption Setting

Develop better assumptions that satisfy inherent dimensional relationships Develop assumptions by channel of acquisition Provide more accurate forecasting for future events (death, lapse, surrender, etc.) Estimate marginal impacts of underwriting requirements

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AI can help you do things better

Example Areas

Assumption setting Projection of claims, lapses, surrenders,

withdrawals Protective Value studies

Segmentation Reinsurance structuring and pricing Stress and scenario testing

How AI Achieves it Supplement actuarial credibility with AI validation assessment metrics Automatically detect and utilize correlations and interactions Tools to capture more reliable, sustainable relationships to minimize overfitting

Develop better assumptions that satisfy inherent dimensional relationships

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Use case - AI can help you do things better

Female, NS* VBT 2015 table

AI can …• Borrow information from “adjacent”

cells along multiple dimensions• Enforce complex relationships

• Select, ultimate periods• Omega rate• Monotonic constraints • …

Develop better assumptions that satisfy inherent dimensional relationships

Duration

Issu

e Ag

e

The cell does not have enough credibility to make any adjustment

With borrowed information from “adjacent” cells, we can be more confident to make adjustment

AI can help you do new things

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Example Areas

Completely fluid-less underwriting New underwriting, pricing process powered by image, text, voice, cognitive AI New capability to provide more granular (closer to individual level) pricing Personalized product

How AI Achieves it Capability to handle large volumes of data Create more powerful features via combinations of image/text/voice/cognitive AI Larger selection of algorithms

Future Use Case:“On-demand Insurance”

Create personalized life products – variable duration, structured riders, unique risks, etc. Determine individualized pricing for the personalized product Determine, gather and process all sources of data on the applicant, coverage and scope

(e.g. IoT, images and voices, social media, circumstance specific risk factors, etc.) Estimate mortality risk for that circumstance (e.g. traveling to foreign countries,

participating in risky events, covering for predefined period, etc.)

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How to get started• Pick someplace to start

• Small enough problem to finish in 3-5 months• Delivers business value

• Spend time defining the business problem • Example:

• Do I want to predict mortality or mimic the underwriter decision in my predictive underwriting model?

• Do I want to optimize response to direct marketing campaigns or response of prospects with the best risk profile?

• Other?• Start with the data you have

• The best source of data is your own. You can more out of it than you think• More data does equal better results…harder to find a needle if the haystack is bigger.• There is always more thing you can find from your current data

• Assemble the right team• Actuaries + Domain Experts + Data Scientist

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Thank You!

If you have more questions on AI, please come see us at Booth 519 in the Exhibit Hall, or contact me at ggupta@quaerainsights.com

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