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AIT Research on Artificial IntelligenceAgenda:• Introductions• Application in Time Series Predictions • Emerging techniques for Mortality modeling, claim modeling, and fraud detection • Applications in underwriting process, claim process and product comparison • Q&A
Blake Hill FSA FCIA, VP Insurance, dacadoo
Blake is leading insurers into the digital age with dacadoo! Blake has developed and launched insurance, savings, investment, and group health products, and through his leadership launching the Vitality program in Canada he implemented partner integrations and the use of advanced analytics in marketing and customer engagement. Blake is now supporting insurance companies globally as they transformation into customer centric and digitally enabled businesses using the dacadoo Health Risk Quantification and dacadoo Digital Health Engagement platform.
Victoria Zhang, FSA FCIA, Associate Director, Sun Life
Victoria Zhang, Associate Director at Sun Life, is a passionate research actuary for the SOA. Her research interest expands from Machine Learning applications in actuarial field, actuarial modeling, to stochastic modeling.
Marie-Claire Koissi-Kouassi , PhD
Marie-Claire Koissi is a Professor of Mathematics, faculty with the Actuarial Sciences Program at the University of Wisconsin – EC. Her research areas include stochastic mortality modelling and forecasting, risk management, and fuzzy logic application to insurance.
Dihui Lai, PhD, ASA, Lead Data Scientist, RGA
Dihui Lai is a Lead Data Scientist in RGA. In this role, Dihui uses machine learning and predictive modeling for various insurance applications, including artificial intelligence (AI)-augmented underwriting, predictive model-based pricing, and lapse experience studies. He has experience in quantitative modeling, nature language processing and document image processing.
A Tour of AI Technologies
in Time Series Prediction
Victoria Zhang, FSA, FCIA
October 29th, 2020
Introduction of AI
What is AI - AI is the techniques that enable
computers to mimic human intelligence It has been used in many fields including speech recognition, self-
driving cars and language translation.
What is Machine Learning - ML is a subset of AI
where a computer system is trained with a large
amount of data to learn how to carry out a specific task.
What is Deep Learning - Deep learning is a
subset of machine learning. It applies neural network which tries to replicate the human brains’ approach to analyzing data.
What can we benefit from AI as an actuary Strong representability
Better Accuracy
Fast Adaptability
Today’s Agenda:
Victoria – Application in Time Series Predictions
Marie-Claire – Emerging techniques for Mortality modeling, claim modeling,
and fraud detection
Dihui – Applications in underwriting process, claim process and product
comparison
Time Series Prediction for Actuaries
What is time series prediction, why do actuaries need it?
What’s the current methodology for time series prediction?
What are the limitations/issues
What do we propose to do? (using AI for time series prediction)
In the following presentation: two Deep Learning examples(DNN and RNN)
DNN: Deep Neural Networks
What is DNN?
Why DNN is better?
How to use DNN?
Types of DNN: CNN (Convolutional Neural Networks)and MLP (Multi-layer
Perceptron)
DNN Example for Bitcoin Price
Data preprocessing(Bitcoin price from January 2012 – March 2019): 2,627 points
Create the model: CNN and MLP
Train the model
DNN Example for Bitcoin price - Result
Model CNN MLP
No. of Parameters ~9000 21,509
Mean Square Error 0.039 0.048
RNN: Recurrent Neural Networks
What is RNN? How is it different than DNN
How RNN is special? Why we consider RNN for time series prediction?
A type of RNN model: LSTM
Summary and Future Work
Open sourced libraries. Many libraries are optimized with GPU, which has
very powerful computing power
Higher accuracy compare to classic statistic models
Emerging research in time series prediction
Emerging Data Analytics Techniques with Actuarial Applications
Marie-Claire Koissi, PhD,Actuarial Science Program, [email protected]
October 29, 2020
Agenda
• Emerging techniques for mortality modeling
• Emerging techniques for claim modeling
• Emerging techniques to detect Insurance fraud
• Trees models• Mortality regression with cause of mortality (Deprez, et al. 2017)• Mortality rates by cancer type (Shang, 2017)• Fit and predict maternity recovery rates and mortality rates (Kopinsky, 2017)
• Neural network• LC model with age specific cohort effects (Hainaut, 2018)• LC model with Multiple Populations (Richman and Wüthrich, 2018)• Mortality rates by cancer type (Shang, 2017)• Mortality Embedding- Entity-Embedding Neural Net (Vincelli, 2019)
• Predictive Modeling
8
Emerging techniques for mortality modeling and forecasting
10
Emerging techniques for mortality: Neural network on the Lee-Carter model for mortality
Multi-population model 𝐥𝐨𝐠 𝒎𝒙,𝒕 = 𝒇 𝒙 + 𝒈 𝒙 𝒉(𝒕)
Deep Neural NetworkUsing back-propagation algorithm
NN learn directly from data
Age embedding with dimension reduction
MSE
LC(Standard) 5.50
LC-DNN 2.68
Question to audience:
In your work, what type of mortality model do you use?
A. Deterministic, with an improvement factor
B. Stochastic
C. Selected mortality table
D. Other
Part 2: Emerging techniques for claim modeling
• Standard method: GLM• Copula regression (idea is to use function that
link univariate marginals to their full multivariate distribution; Frees and Valdez, 1998)
• Popular in recent studies:Trees modelsNeural network
• Predictive Modeling
Emerging techniques:Modeling Claim in Motor Insurance using Regression Trees(Noll et al., 2018)
Variables of Interest:
Vehicle features:
Driver features:
Other variables: Area code (categorical)Number of claims on policy i: Ni
Vehicle power (categorical)Vehicle ageVehicle gas (categorical)Vehicle brand
Driver Age
Model:
𝑁𝑖 ~ Poisson (𝜆 𝑥𝑖 𝐸𝑖)
Where log(𝜆 𝑥𝑖 ) = 𝛽𝑜 + σ𝑖 𝛽𝑖𝑥𝑖
Part 3: Emerging techniques to detect Insurance fraud
• Data balancing method known as Adaptive Synthetic Sampling Approach for Imbalanced Learning.
• Clustering, K-mean• Tree models
Note: Circular symbol denotes a legitimate claim and a trianglea questionable claimR-function for Multi-Dimensional Scaling: cmdscale
Machine Learning is used to order claims along risk classes
Question to audience:
In your work, do you use this software?
A. R software, quite often
B. Python, quite often
C. R or Python, but rarely
D. Never use R or Python
Takeaways
• Machine learning aims at improving model efficiency and “easiness” to use.
• Machine learning help user in handling large data set and extracting relevant information (which will otherwise be disregarded).
• Lot of work still needs to be done, but possible applications in actuarial work are promising.
16
Mortality modelling packages in R• Lifemetrics (Cairns et al., 2006)
• The Lifecontingencies package (Spedicato, 2013)
• Demography (Hyndman 2014)
• ilc (iterative LC) (Butt, Haberman, and Shang 2014)
• StMoMo (Stochastic Mortality Modelling, by Villegas, Millossovich and Kaishev, 2018, most recent
19
Mortality modelling packages in Python
• Python has a full library of packages for actuarial science and survival analysis including: Lifelines
Traditional Mortality Models(for purpose of information / reminder only)
• De Moivre (1729):
• Gompertz (1825):
• Makeham (1860):
• Weibull (1951):
• Kannisto (1992):
• Heligman & Pollard (1980): (8 parameters)
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x ax=
bx
bx
xae
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++=
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xFxEBx
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Contents
• Underwriting Process and AI
• Digital Health Data and NLP
• CI Coverage Comparison
• Claim Operations
AI Augmented Underwriting Process
Data Sources Stage 1 – Data Conversion,
Extraction, & Linkage
Quick
Decline
UW
decision
Stage 2 – Risk Assessment
Aggregate data from multiple
sources in multiple formats;
convert into structured,
linked, & interpretable data
Intelligent
Summary
Case triage
AI Function
• Intelligent Decision
• Recommendation
• Medical Interpret
• Warning/Red Flag
Additional requirement
STP/RuleETL
Fully UW
Feedback
NLP
Accelerated
UW
AI Function
• Info Extraction
• Data Linkage
• Unstructured → Structured
Underwriting Profile Summary
AI Augmented Underwriting Process: Summary
• Extract demographic information
from multiple sources (PDF, xml
etc.)
• Provide an overview of the
applicants’ underwriting profile
• Assess the risks for multiple
cohorts: lab, prescription drug, APS,
driving records etc.
• Provide severity score if possible
• Linkages to the source information
e.g. BMI
Digital Health Data (DHD) and NLP
Medical Entities
Unstructured DHD
Structured Process
51
DHD Description
rechk hasn’t passed kidney stone yet
• DHD contains both structured/unstructured data
• Unstructured data contains comprehensive information about individual health
• Read through all unstructured data is time consuming
• Unstructured data could contain information that is not covered in the structured data
• In one study, we use NLP and are able to identify ~15% of the cases that contain more severe
medical conditions from unstructured text.
Medical Severity
ICD Code [N20.0]
UW Impact
52
Product Definition Comparison
When it comes to CI product, companies share a lot of common features however, every company
could have varied definitions on detailed exclusions, time span, surgery procedures etc. The
review process of comparing CI definitions could be challenging and tedious. Could NLP offer
some help?
Surgery to Aorta - Definition 1
Surgery to Aorta - Definition 2 Similarity: 0.167
Claim Notes Classification
Sample Claim Notes: 2012.5.6投保822,2016.12.16投保1183、1185,本次于2017.10.27因脑白质病变申请理赔,本次疾病不符合条款约定的重疾,无对应重疾责任,已协谈,本次拒付处理。。脑白质病变 脑白质病变;。脑白质病变
Claim
Note
Benefit
Classifier
ESCI LSCI DEA
ESCI
Disease
identifier
LSCI
Disease
identifier
DEA
Disease
identifier
Use machine leaning based model to
categorize the claim notes into
appropriate type.
Question
• Insurance and other traditional areas where Actuaries work are relatively data poor compared to new digital enterprises, such as Google, Tesla, Amazon. How will Actuaries need to evolve to work with (partner) or for these enterprises?