Tag Archives: pricing game

Actuarial Pricing Game

Within a few days, we will launch the fourth actuarial pricing game.

  1. People have to register for that new game. People have to fill a form online before February 28th to get a training dataset. As in our previous versions, players can be individuals or in a team (identified as one player).
  2. When registration closes, players will receive a training dataset (training.csv). The training dataset will be based on two years of data on household policies. There will be information about the insurance policy from underwriters, as well as, claims data. They will also receive a pricing dataset (pricing.csv) with only underwriters information and no claims. The goal is to propose a premium for all records in the pricing dataset. Note that those registered will also receive additional information and template files after registration closes.
  3. Before  April 9th, 2018, players will have to provide two files: a pricing function (either a formula.py Python function, or a formula.R R function) and a dataset with prices prices.csv). The pricing function should be an understandable explicit function which, once applied to the training dataset, will yield premium prices for each record. There will be limits on the complexity of this function. To ensure this, players will receive instructions on what can be used to construct formulas either in Python or R.
  4. Before May 14th, 2018, players are asked again to submit two files: a dataset with prices (second_prices.csv) and a pricing model (either a model.py Python file, or a model.R R file). For this round there are no restrictions on what can be used in the model, as long as, by using the model file submitted the prices dataset (second_prices.csv) can be reproduced. Players will again receive further instruction on this after registration.

Insurance: Risk Pooling and Price Segmentation

Talk this afternoon at the seminar of Telecom ParisTech

Insurance is usually defined as “the contribution of the many to the misfortune of the few”. This idea of pooling risks together using the law of large number legitimates the use of  the expected value as actuarial “fair” premium. In the context of heterogeneous risks, nevertheless, it is possible to legitimate price segmentation based on observable characteristics. But nowadays, intensive segmentation can be observed, with a much wider range of offered premium, on a given portfolio. In this talk, we will briefly get back on statistical approaches of insurance pricing (classical econometric tools vs machine learning). We will then get back on recent experiments (so-called “actuarial pricing game”) organized since 2015, where real actuaries are playing in competitive (artificial) market, that mimic real insurance market. We will get back on conclusions obtained on two editions, the first one, and the most recent one, where a dynamic version of the game was launched.

By the way, there will be soon a fourth version of the “Actuarial Pricing Game”… some information soon, on this blog…

Actuarial Pricing Game, with Reinsurance

The Third Actuarial Pricing Game is still open, the deadline for submission is still February 25th. As mentioned in the instructions, for those willing to play in a market where reinsurance is available, here are the prices offered by some reinsurance company.

As mentioned in the description, the price is per insurance policy, per year. Players should send me their premiums in a csv file, gross of reinsurance, and mention in the email that they want to purchase treaty (A) – for instance (and mention explicitly in the object of the email that they want to play in this specific market, where reinsurance is available).

Third Actuarial Pricing Game

With the support of ACTINFO Chair and the (French) Institute of Actuaries, our Third Actuarial Pricing Game starts today ! There is a toolbox file available online, with

  • a description of the game : the rules, the dates, and a description of the datasets
  • 3 datasets : one underwriting and one claims databases, for year 0 (training data) and one underwriting dataset to enter the game

Anyone can play. Students from various programs around the world, as well as practitioners are welcome to play. It can be by teams, and there are no limit on the size. And there is no registration: to start playing, teams have to submit a dataset before the deadline (end of February), to pricing-game@univ-rennes1.fr.

Teasing for the Third Actuarial Pricing Game

We will launch within the next few days the Third Actuarial Pricing Game. The goal will be to replicate the behavior of insurance markets over time. There will be a first stage (January-February) where players will have to build a pricing model for motor insurance policies on the basis of 50,000 contracts observed Year 0 (characteristics of contracts, policyholder, and claims datasets). Proposals for premiums for Year 1 will have to be provided for the same contracts (updated for the age, the location, the car model, etc.). Players will use only the underwriting datasets to provide premiums for Year 1.

Between February 25th (deadline for the proposal for premiums) and March 1st, we will replicate an insurance market by creating competition among insurers (players), and by setting simple rules to match drivers and insurers (randomly among the cheapest at the beginning of the game, and then, as time goes by, we will add inertia, i.e. a tendency to stay with the same insurer if the latter is not too expensive). Once the drivers and the playees are matched we will provide claims information for the insured that each player has won. A new premium proposal must then be submitted at the end of Year 1 (i.e. at the end of March). Etc.

We will here try to replicate the a car insurance market over four years. The goal (for players) will be to maximize the profit of the insurance company over those four years. To make the game more realistic, insurers are assumed to have capital, and they can remain in the game only if their yearly loss ratio (claims over premium) is less than 150%. More information next week (rules, and training datasets).

Pricing Game, c’est reparti

Pour la seconde année consécutive, on organise un pricing game dans le cadre de la formation Data Science pour l’Actuariat. Les instructions sont en ligne. Pour information, il y a trois bases en ligne

  • train_contrats:  87 228 observations, 22 variables. Base contenant les informations relatives aux polices d’assurance
  • train_sinistres:  4 568 observations, 9 variables. Base contenant les informations relatives aux sinistres observés
  • test_contrats32 772 observations, 21 variables. Base contenant les informations relatives aux polices d’assurance pour lesquelles il conviendra de proposer une prime

La première étape du jeu va durer 2 mois (entre le 15 juillet et le 15 septembre). Il s’agira de proposer 32 772 primes d’assurance. Il est impératif de proposer une prime pour tous les contrats. Autre information supplémentaire, pour tous les joueurs, la somme des primes sera égalisée à 2,5 millions d’euros.

Petite nouveauté, il y aura une seconde étape ensuite. Chaque participant recevra la prime de deux de ses concurrents, et aura la possibilité de mettre à jour ses primes, et de renvoyer des primes mises à jour d’ici le 20 septembre.

Les commentaires sont ouverts pour tout complément d’information ! J’essayerais de poster des informations complémentaires, en particulier sur la description des variables, au cours des vacances. L’analyse du jeu sera proposée lors de la journée 100% Data Science de l’Institut des Actuaires (qui aura lieu le 8 novembre 2016). Bon courage en attendant !