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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
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
Society of Actuaries Predictive Analytics Seminar 3
My team
Actuaries
Technology specialists
Business strategists
Data managers
Statisticians
Data scientists
I work with …
My role is …
Society of Actuaries Predictive Analytics Seminar 4
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.”
Society of Actuaries Predictive Analytics Seminar 5
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%!
Society of Actuaries Predictive Analytics Seminar 6
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
Society of Actuaries Predictive Analytics Seminar 7
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
Society of Actuaries Predictive Analytics Seminar 8
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
Society of Actuaries Predictive Analytics Seminar 9
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
Society of Actuaries Predictive Analytics Seminar 10
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
ModelsVALUES predictive model Baseline predictive model
Rank of relative probabilities
Comparison of full predictive model to baseline predictive model
Society of Actuaries Predictive Analytics Seminar 11
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
Society of Actuaries Predictive Analytics Seminar 12
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
Society of Actuaries Predictive Analytics Seminar 13
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
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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.
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)
4
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
• 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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