Xavier Conort, DataScience SG Meetup - Challenges in insurance pricing

1. Modeling Challenges for
insurance pricing
Xavier Conort
Chief Data Scientist - DataRobot
Tuesday, February 23, 2016 @

2. Agenda
● Preamble
● Unfriendly distribution shape of claims cost
● Regulation or operational constraints
● Need to predict the future!
● Claims that are Incurred But Not Reported or Not
Enough Reported (IBNR, IBNER)

3. Automation is integral part of human civilization

4. ● Ivy league approach - only for the chosen ones
● Focused on activities - detached from outcomes
● Assumption based: model selection is based on
modeler‘s understanding of the world?
● Development is costly and limited
● Heavy dependence on programmers
Next generation tools, platforms, approaches to data science
Traditional Approach Data Science Approach
● Common man approach - for everyone
● Focused on business outcome
● Validation based: model selected if it predicts well in
real world
● Development is crowd sourced, peer reviewed
● Automated solutions are taking care of programming
open source
programming
social network
of coders
automated
solutions

5. “90% of the data in the world today has been created in the
last two years alone”
and some companies have been very successful in
using data thanks to data science
better
service
newer
product
improved
operations

6. SO WHY THE ROBOT HAS NOT
REPLACED THE ACTUARY YET ?

7. No Open Data => Slower innovation

8. Unfriendly distribution shape
● Low Claim frequency
● Claim severity follows a skewed distribution
with sometimes a large tail
● And ...
○ Discontinuities caused by policy limits
○ Environmental and operational changes leading to distributions
constantly changing over time
○ Heterogeneity of risk within risk pools, caused by fraud and
imperfect measures of risk exposure
○ ...

9. info@DataRobot.com | @DataRobot | DataRobot, INC., CONFIDENTIAL
OUR APPROACH TO WIN 1st PLACE IN:
High severity (presence of
large claims)
Low frequency (0.26% of
claims event)
Many Features
Censor large claims
Downsample majority
events
Get rid of noise
Let DataRobot explore the best
transformations and the Machine
Learning Algorithms for the data
Experiment other models in R

10. A more actuarial approach
● Modeling per risk type (bodily injury, damage, 3rd party...)
● Censoring large is well accepted but don’t forget to reallocate the cost
of large claims if you use your model for pricing
● Log transformation and downsampling are less a practise. If you do
this, don’t forget to adjust the bias
● Actuaries will mostly use GLMs (Generalized Linear Models with Poisson, Gamma
and Tweedie distributions with a “log” link) and do either:
○ frequency modeling x severity modeling but be aware this makes the strong
assumption that frequency and severity are independent.
○ or cost modeling directly

11. Can ML algos support Poisson,
Gamma and Tweedie loss functions?
● Yes! As an example, Regularized GLMs and Gradient Boosting Machine
(GBMs) can support any exponential distributions
● But … open source implementation rarely support Gamma and
Tweedie loss functions…
● Good news!
○ Poisson loss function is supported by XGboost, R gbm and R
glmnet
○ open source algorithms are open! so you can patch them. In some
cases, only a few lines of code are necessary

12. Regulation or operational constraints
Regulation or operational constraints might force you
to keep control of the model built or/and keep it simple
➢ Example of sum insured: The premium should be monotonically increasing with sum
insured, otherwise people will just purchase more cover and pay less...
➢ Many insurance companies use pricing tables
❖ use ML for feature selection and get insight on non linear relationship and interaction. And
then integrate this insight into your GLMs where you have full control
❖ eliminate undesirable features
❖ patch ML algos to add monotonicity (R gbm already does it!) or interaction constraints

13. Predict the future
sum insured
history
decision tree prediction
actual risk
new values
Models in general are not very good in predicting the future
2 things to keep in mind when you are using GBMs or RFs or bin the
continuous variables...:
● decision trees don’t extrapolate new values. They won't predict higher
claim sizes for sums insured higher than history
● Machine can be naive and lazy
○ see 2 examples in next slide

14. Naive Machine
● one example is GE Flight Quest. To win the competition, my I2
R
colleagues and I censored the info of the name of the airports to force
Gradient Boosting Machine
○ to learn the reasons of the delay
○ and not to learn that one airport never had delays in the past 3 months and conclude
it will never have delays in the future
● A real life example of this in insurance is when an actuary uses the policy
number as one of the features to predict the claims cost. A naive
predictive model could use policy number as a proxy for both inflationary
effects and tenure effects.

15. Backtesting
To get good prediction models, you need to be fully aware of the model
limitations and effects changing over time.
To get this insight, Backtesting is an important step
Backtesting is the process of applying an or analytical method to historical data to
see how accurately the method would have predicted actual results.
It should help you uncover underperformance due to poor modeling or
environmental and operational changes.
Machine Learning can be used to automate this experience analysis. And
this time, there is no regulation or operational constraints!

16. IBNR and IBNER
Long claim developments make the modeling process harder
● It can take a long time to know an event happened and even longer to know the final cost.
The life cycle of a claim typically includes event occurrence, reporting, initial estimation,
case estimate review, payment, recoveries... and the reported losses are usually uncertain
● Risks with such long claims are called long-tail risks. Personal injury compensation schemes
(workers’ compensation and motor accident insurance) are typically long-tail risks.
Advices:
● first focus on short tail risks
● recruit an experienced actuary
● or more exciting! estimate IBNER on individual claims using machine learning

17. Key takeaways
● Modeling insurance pricing is not easy
● Machine Learning can definitely help in the modeling process
● Open source solutions are not designed for insurance pricing and
innovation will be slower as insurance is not an open world
● Plenty of other exciting modeling projects in insurance:
○ Scan through millions of potential consumers to choose the right few
○ Predict lapses and build price elasticity models
○ Select top 10% of your selected risk to manually review / inspect further
○ Identify claimants with highest likelihood of being fraud and review them manually
○ Text mine the beneficiary clause of life insurance contracts
○ Estimate which claims are likely to become problem claims that require special attention
○ Decide who should handle a claim or to pay it without further checks
○ Geographic features / spacial smoothing
○ Detect changes in mix of business
○ ….

18. Automation is inevitable
The
Economist,
May 2015
Harvard
Business
Review,
June 2015