Distinguishing the forest from the trees 2006 cas ratemaking seminar
Download
1 / 26

Distinguishing the Forest from the Trees 2006 CAS Ratemaking Seminar - PowerPoint PPT Presentation


  • 81 Views
  • Uploaded on

Distinguishing the Forest from the Trees 2006 CAS Ratemaking Seminar. Richard Derrig, PhD, Opal Consulting www.opalconsulting.com Louise Francis, FCAS, MAAA Francis Analytics and Actuarial Data Mining, Inc. www.data-mines.com. Data Mining.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Distinguishing the Forest from the Trees 2006 CAS Ratemaking Seminar' - deborah-meyers


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Distinguishing the forest from the trees 2006 cas ratemaking seminar

Distinguishing the Forest from the Trees2006 CAS Ratemaking Seminar

Richard Derrig, PhD,

Opal Consulting

www.opalconsulting.com

Louise Francis, FCAS, MAAA

Francis Analytics and Actuarial Data Mining, Inc.

www.data-mines.com


Data mining
Data Mining

  • Data Mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. In order to achieve this, data mining uses computational techniques from statistics, machine learning and pattern recognition.

    • www.wikipedia.org



An insurance nonlinear function provider bill vs probability of independent medical exam

An Insurance Nonlinear Function:Provider Bill vs. Probability of Independent Medical Exam


Common links for glms
Common Links for GLMs

The identity link: h(Y) = Y

The log link: h(Y) = ln(Y)

The inverse link: h(Y) =

The logit link: h(Y) =

The probit link: h(Y) =



Desirable features of a data mining method
Desirable Features of a Data Mining Method:

  • Any nonlinear relationship can be approximated

  • A method that works when the form of the nonlinearity is unknown

  • The effect of interactions can be easily determined and incorporated into the model

  • The method generalizes well on out-of sample data


Decision trees
Decision Trees

  • In decision theory (for example risk management), a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal. Decision trees are constructed in order to help with making decisions. A decision tree is a special form of tree structure.

    • www.wikipedia.org


Different kinds of decision trees
Different Kinds of Decision Trees

  • Single Trees (CART, CHAID)

  • Ensemble Trees, a more recent development (TREENET, RANDOM FOREST)

    • A composite or weighted average of many trees (perhaps 100 or more)

    • There are many methods to fit the trees and prevent overfitting

      • Boosting: Iminer Ensemble and Treenet

      • Bagging: Random Forest


The methods and software evaluated
The Methods and Software Evaluated

1) TREENET 5) Iminer Ensemble

2) Iminer Tree 6) Random Forest

3) SPLUS Tree 7) Naïve Bayes (Baseline)

4) CART 8) Logistic (Baseline)


Cart example of parent and children nodes total paid as a function of provider 2 bill
CART Example of Parent and Children NodesTotal Paid as a Function of Provider 2 Bill


Cart example with seven nodes ime proportion as a function of provider 2 bill
CART Example with Seven Nodes IME Proportion as a Function of Provider 2 Bill


Cart example with seven step functions ime proportions as a function of provider 2 bill
CART Example with Seven Step FunctionsIME Proportions as a Function of Provider 2 Bill




Naive bayes
Naive Bayes

  • Naive Bayes assumes conditional independence

  • Probability that an observation will have a specific set of values for the independent variables is the product of the conditional probabilities of observing each of the values given category cj




The fraud surrogates used as dependent variables
The Fraud Surrogates used as Dependent Variables Provider 2 Bill

  • Independent Medical Exam (IME) requested

  • Special Investigation Unit (SIU) referral

  • IME successful

  • SIU successful

  • DATA: Detailed Auto Injury Claim Database for Massachusetts

  • Accident Years (1995-1997)


Results for ime requested
Results for IME Requested Provider 2 Bill


Treenet roc curve ime auroc 0 701
TREENET ROC Curve – IME Provider 2 BillAUROC = 0.701


Ranking of methods software 1 st two surrogates
Ranking of Methods/Software – 1 Provider 2 Billst Two Surrogates





References
References Provider 2 Bill

  • Brieman, L., J. Freidman, R. Olshen, and C. Stone, Classification and Regression Trees, Chapman Hall, 1993.

  • Friedman, J., Greedy Function Approximation: The Gradient Boosting Machine, Annals of Statistics, 2001.

  • Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York., 2001.

  • Kantardzic, M., Data Mining, John Wiley and Sons, 2003.


ad