2017 Predictive Analytics Symposium - soa.org · Predictive Analytics Seminar 14 Key Takeaways Predictive models are well suited to investigating policyholder experience data Reduced

2017 Predictive Analytics Symposium

Session 29, Predictive Analytics for Inforce Management

Moderator: Rohan Noel Alahakone, ASA, MAAA

Presenters:

Jenny Jin, FSA, MAAA Assaf Mizan

Martin Snow, FSA, MAAA

SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer

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SOA Predictive Analytics SeminarSession 29: Predictive Modeling for Inforce Management

Jenny Jin, FSA, MAAA

15, Sept. 2017

Society of Actuaries Predictive Analytics Seminar 2

Agenda

Motivation for Predictive Models in Inforce Management

Examples


My team

Actuaries

Technology specialists

Business strategists

Data managers

Statisticians

Data scientists

I work with …

My role is …


Industry Recognition of the Problem

Moody’s Investors Service Unpredictable Policyholder Behavior Challenges US Life Insurers’ Variable Annuity Business

“Though equity-market declines are generally seen as the biggest risk in VA contracts, most insurers effectively hedge that risk via derivatives. That leaves the less-easily hedged and more unpredictable policyholder behavior, and particularly lapses, as a key driver of the profitability of these popular products.”

“Companies selling VAs misestimated and underpriced lapse risk. Retention by policyholders of these guaranteed products was much greater than expected, causing insurers to take significant, unexpected earnings charges and write-downs over the past year and a half.”

“Recent experience for these guarantees provides [the takeaway that] … Companies tend to retain customers that cost them the most and lose those that cost them the least.”


What does tabular analysis really tell us?

Source: FlowingData.com, Wikipedia

Descriptive analysis takes data and summarizes them using an average metric for the cohort but sometimes can be misleading if there are confounding variables

Actuaries have the lowest divorce rate at 17%!


Milliman VALUES Industry Study Data Set

VALUES Industry Lapse Study covered 117 million quarterly observations, $500bn AV VALUES GLWB

Utilization Study, 2 million policies, $200bn AV

70% training set

30% holdout set


Lapse Models: baseline and alternative implementations

Baseline model

Baseline predictive model

Milliman VALUES predictive model

LapseBase Ratef(q)

ITM Factorf(ITM)

Log Odds

qITM

Factorf(ITM)

k1 k2

Log Odds q

ITM Factorf(ITM)

k'1 k'2

Tabular approach

GLM regression model


Milliman VALUES Lapse StudyPost surrender charge lapse experience generally lower than current assumption

Less guess work on the effect of base vs dynamic lapse

Predictive model provides a single framework for analyzing and attributing the impact to both duration and moneyness jointly.

Irrational

More rational

Closest to actual experience


Algorithms can help accelerate variable selection

-4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5Multiplicative impact to lapse rates

Relative importance of new predictors

CategoryBehaviorDemographicDistributionPolicy sizePolicy state

When faced with hundreds of potential variables, computers algorithms are much faster at selecting important variables based on their influence on the behavior


2 4 6 8 10 12 14 16 18 200.0

0.5

1.0

1.5

2.0

Act

GWB

ModelsBaseline modelBaseline predictive model

Predictive model improves predictions

Comparison of baseline tabular model to baseline predictive model

Rank of relative probabilities2 4 6 8 10 12 14 16 18 20

0

1

2

3

4

Act

.

GWB

ModelsVALUES predictive model Baseline predictive model

Rank of relative probabilities

Comparison of full predictive model to baseline predictive model


WB Withdrawals: Takeaways

• Policyholders who are older at issue tend to utilize their policies sooner

• Qualified policyholders will start their withdrawals sooner after age 70

• Less than half of all policyholders currently taking GLWB withdrawals utilize their GLWB benefit with 100% efficiency

• Utilization inefficiency is a driver of lapse

Proprietary and Confidential


What else can we learn about the customers?

Enhanced Dataset

Vendor Data

$

$Analytics

Actuarial Assumptions by Segment

Customer Segmentation

Policy Level Customer Value

Insurance Company Data- Policy values- Product features- Policy behavior

ConsumerData

Credit Data

Vendor Data

Outputs

Mortgage Data

Census Data

Health Score

Rx


Annuity behavior modeling: progression of states

DescriptiveWhat happened in the past

DiagnosticWhy did it happen?

PredictiveWhat may happen?

PrescriptiveWhat can be done?

Business Value

Com

plex

ity

HighLow

High

Hindsight

Foresight

Insight

Companies are evolving on the analytics spectrum from descriptive and diagnostic analysis to predictive and prescriptive analysis.

Next-generation experience studies will use much wider set of explanatory variables and more sophisticated analysis techniques to find non-linear, multivariate effects, complex interactions

Insights from experience studies can be used to develop individual policyholder profiles and to drive product development and create positive engagement with customers


Key Takeaways

Predictive models are well suited to investigating policyholder experience data

Reduced cost of storage and computing has largely lifted constraints around predictive modelling methods and applications

Actuarial judgement is still required, in particular to avoid creating models that are hard to interpret or implement.

A multidisciplinary team is necessary to successfully advance in this new area: Subject matter expertise in the products, policyholder use of products, and the financial implications to insurers. Data managers Data scientists Technology developers IT infrastructure

Start with the low hanging fruits such as maintaining high quality data and collecting supplementary data sources and grow from there.

Thank You!Jenny JinLife/FRM – Consulting Actuary – Chicago [email protected] 499 5722

mailto:[email protected]

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Predictive Analytics Symposium

Predictive Analytics for In Force Management Session 29 – Some Practical ExamplesSeptember 15, 2017

Assaf Mizan, FIA, FILAA

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Why predictive analytics

• Predictive Analytics in insurance:

Use of advance modelling and data mining tools to predict

behavior in the insurance space

• Contrasted to traditional actuarial methods, these tools have important advantages in the insurance field, in particular:

• Few constraints on the volume of data and features used in the analysis

• Lower requirement for clean data

• Capture complex interactions

• Require significant processing power and data storage, but these are now much cheaper

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Overall flow

Additional TablesOne view of the client

Policy Admin System

Premium Collection

Customer Rel-ship

Management

Business question

Historical / Snapshot data

Feature engineering

External data enhancement

The modeling Arena

Results & Insights Actions

Validation

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Business Question and Modelling

Example 1 - Persistency

• High level business question:

Why are policyholders lapsing?

• Target model:

Fit a model to the historical persistency of each policy.

Several models can be fitted – depending on product complexity, variation by policy duration, data availability etc. The model can deliver insights as to the influencing features and rank current policies based on their propensity to lapse (now and in the future)

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Example 2 - Underinsurance

• High level business question:

Is there an underinsurance opportunity?

• Target model:

Cluster policyholders based on similar characteristics – policy type, policyholder characteristics, external data.

Find outliers which have significantly low sum assured compared to peers.

• Important to set clear validation goal to ensure model is adding value

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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)

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Feature engineering - examples

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How would we incorporate the sale date 24/11/2010 ?Feature Meaning

Date (number) Models long term trends

November Month-specific sale features, year end targets

24th day of the month Month end targets?

Monday Significance of week days

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 recent to the current date?

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

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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 class is significant to lapsation

- Another enhancement – connect to underwriting risk classification

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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, financial data can also be useful

• Lifetime events, from external sources, can be useful also triggers, for example:

• house purchase

• Job change

• New family member

• Company data which is “external” to the book 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!

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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 to three categories:

• External

• Indirect

• Direct

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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 upsale:

• Either when the client approaches the company

• Or as a selective pro-active approach

• 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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Some closing thoughts

• Predictive analytics can be a powerful tool in managing inforce business

• Results depend on availability of data. Frequency of client contact impact results. Use of external event data can substitute lacking 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 (eg 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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Contact details

Assaf Mizan FIA, FILAA

VP Product and Actuary

Atidot Ltd

[email protected]

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Predictive Analytics Symposium

Predictive Analytics for In Force Management Session 29September 15, 2017

Martin Snow, FSA, MAAA

1© Copyright by SOA

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Presentation Disclaimer

Presentations are intended for educational purposes only and do not replace independent professional judgment. Statements of fact and opinions expressed are those of the participants individually and, unless expressly stated to the contrary, are not the opinion or position of the Society of Actuaries, its cosponsors or its committees. The Society of Actuaries does not endorse or approve, and assumes no responsibility for, the content, accuracy or completeness of the information presented. Attendees should note that the sessions are audio-recorded and may be published in various media, including print, audio and video formats without further notice.

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Predictive Analytics for In Force Management - Introduction

• Interest in Predictive Analytics (PA) for In Force Management is increasing

• Yet, significantly more can be done:

• More companies can become engaged

• Engaged companies can do more use cases

• Projects can move from the pilot phase to implementation

• Types of in force management use cases today

• Envisioning future projects

• Who is doing the work? How can actuaries do more?

• How is success defined for a PA project?

• How to integrate multiple PA initiatives one with another

• What are the top obstacles to success and what can be done about them?

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Predictive Analytics for In Force Management

How Insurers Use PA Today

• Increase market penetration

• Expand customer relationships

• Develop accelerated underwriting programs

• Set mortality and policyholder behavior assumptions

• In force management

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Prevalence of Insurer Use of PA for In Force Management

https://www.soa.org/Files/Research/Exp-Study/predictive-analytics-underwriting-report.pdf

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https://www.soa.org/Files/Research/Exp-Study/predictive-analytics-underwriting-report.pdf

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In Force Management Use Cases Today• Targeted conversion

• Post-level premium term conservation management

• In force management – pre-lapse

• In force management – post-lapse

• In force management – other customer interaction

• Agent monitoring/management

• Up-sell

• Buy-outs on non-profitable customers

• Other

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Who does PA Work for Inforce Management?

Who leads the work?

• Internal vs. External

• Marketing, Data Science / Statistics, Actuary

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How is Success Defined?

• SOA Report does not define success

• What criteria can be used to define success?

• What criteria are companies using?

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Integration of Multiple PA Initiatives

• Rationalize

• Obtain

• Identify

• Vision

• Core Competency

• Milestones

https://www.linkedin.com/feed/update/urn:li:activity:6285162336839888897

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https://www.linkedin.com/feed/update/urn:li:activity:6285162336839888897

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Top Obstacles to Success

• Human behavior

• Legacy Infrastructure

• Buy-in

• Competing Priorities

• Cost-benefit

• Data sources, implementation, designing and building the model

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Overcoming the Obstacles to Success

• Compelling Business Case

• Expert Opinions

• Carefully designed policyholder surveys

• Learn from products with similar characteristics

• Bayesian models

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Presenter Contact Information

Martin Snow, FSA, [email protected](732) 336-1130

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Documents

2017 Predictive Analytics Symposium - soa.org · Predictive Analytics Seminar 14 Key Takeaways Predictive models are well suited to investigating policyholder experience data Reduced