# Fairness and discrimination, PhD Course, #2 Insurance and risk classes

For the second course, we will get back a little bit on insurance pricing in a context of heterogeneous portfolio, and risk classification (slides are still online on the github repository). The starting point will be the pure premium.

See our online textbook, with Michel Denuit, Non Life Insurance Mathematics, for additional motivation. If we have some risk related variables $\boldsymbol{x}=(x_1,\cdots,x_k)$, the pure premium will be the conditional expectation,

Here also, we have some law of numbers, for the conditional expected value,

This relationship, which defines the conditional expected value using the limiting value of a conditional frequency cannot be used to define properly $\mathbb{P}[Y|\boldsymbol{X}=\boldsymbol{x}]$ and $\mathbb{E}[Y|\boldsymbol{X}=\boldsymbol{x}]$. One can consider a limit,$$\mathbb{P}\big(Y\in \mathcal{A}\big\vert X = x\big)=\lim_{\epsilon\to0}\frac{\mathbb{P}(\{Y\in \mathcal{A}\}\cap\{|X -x|\leq \epsilon\})}{\mathbb{P}(\{|X -x|\leq \epsilon\})}$$or$$\mathbb{P}\big(Y\in \mathcal{A}\big\vert X = x\big)=\lim_{\epsilon\to0}\mathbb{P}\big(Y\in \mathcal{A}\big\vert |X -x|\leq \epsilon\big)$$as in the law of the unconscious statistician or as Proschan and Presnell (1998) wrote it

statisticians make liberal use of conditioning arguments to shorten what would otherwise be long proofs

We can now compute conditional frequency, given some risk characteristics, for some quantity of interest $y$, such as the age of death, in life insurance contracts.

#### Demographic risk and heterogeneity

First, we will see some gender-based life tables, starting with the one obtained by Nicolaas Struyck (see e.g. Alberts et al. (2014))

More recently, in France, some wealth based life tables were obtained, with various quantiles

And finally, we will see some life tables obtained 50 years ago in the US, with racial distinction

#### Mean and variance decomposition

About pure premiums, an important property is the law of total expectations, and a desirable property, that we will name “balance property”

We will also mention variance and variance decomposition, depending if we take into heterogeneity, or not. With homogenous pricing, we have

If we use the “true” underlying risk factor, $\Theta$, we have the standard variance decomposition, also called law of total variance

i.e.

And finally, if we do not observe $\Theta$, but we have a collection of covariates, $\boldsymbol{X}=(X_1,\cdots,X_k)$,

#### Some historical perspectives

In the textbook, Insurance: Biases, Discrimination and Fairness, I have several paragraph about an historical perspective, starting with insurance as clubs, without segmentation. Then segmentation started, with risk classes and groups. For example, according to Issues And Needed Improvements In State Regulation Of The Insurance Business, by Harry Havens, in 1979,

The price which a person pays for automobile insurance depends on age, sex, marital status, place of residence and other factors. This risk classification system produces widely differing prices for the same coverage for different people. Questions have been raised about the fairness of this system, and especially about its reliability as a predictor of risk for a particular individual. While we have not tried to judge the propriety of these groupings, and the resulting price differences, we believe that the questions about them warrant careful consideration by the State insurance departments. In most States the authority to examine classification plans is based on the requirement that insurance rates are neither inadequate, excessive, nor unfairly discriminatory. The only criterion for approving classifications in most States is that the classifications be statistically justified — that is, that they reasonably reflect loss experience. Relative rates with respect to age, sex, and marital status are based on the analysis of national data. A youthful male driver, for example, is charged twice as much as an older driver all over the country} (…) t has also been claimed that insurance companies engage in redlining – the arbitrary denial of insurance to everyone living in a particular neighborhood. Community groups and others have complained that State regulators have not been diligent in preventing redlining and other forms of improper discrimination that make insurance unavailable in certain areas. In addition to outright refusals to insure, geographic discrimination can include such practices as: selective placement of agents to reduce business in some areas, terminating agents and not renewing their book of business, pricing insurance at un-affordable levels, and instructing agents to avoid certain areas. We reviewed what the State insurance departments were doing in response to these problem. To determine if redlining exists, it is necessary to collect data on a geographic oasis. Such data should include current insurance policies, new policies being written, cancellations, and non-renewals. It is also important to examine data on losses by neighborhoods within existing rating territories because marked discrepancies within territories would cast doubt on the validity of territorial boundaries. Yet, not even a fifth of the States collect anything other than loss data, and that data is gathered on a territory-wide basis.

According to The Role of Risk Classification in Property and Casualty Insurance: A Study of the Risk Assessment Process : Final Report, by Barbara Casey, Jacques Pezier and Carl Spetzler, in 1976,

On the other hand, the opinion that distinctions based on sex, or any other group variable, necessarily violate individual rights reflects ignorance of the basic rules of logical inference in that it would arbitrarily forbid the use of relevant information. It would be equally fallacious to reject a classification system based on socially acceptable variables because the results appear discriminatory. For example, a classification system may be built on use of car, mileage, merit rating, and other variables, excluding sex. However, when verifying the average rates according to sex one may discover significant differences between males and females. Refusing to allow such differences would be attempting to distort reality by choosing to be selectively blind. The use of rating territories is a case in point. Geographical divisions, however designed, are often correlated with socio-demographic factors such as income level and race because of natural aggregation or forced segregation according to these factors. Again we conclude that insurance companies should be free to delineate territories and assess territorial differences as well as they can. At the same time, insurance companies should recognize that it is in their best interest to be objective and use clearly relevant factors to define territories lest they be accused of invidious discrimination by the public. (…) One possible standard does exist for exception to the counsel that particular rating variables should not be proscribed. What we have called equal treatment’ standard of fairness may precipitate a societal decision that the process of differentiating among individuals on the basis of certain variables is discriminatory and intolerable. This type of decision should be made on a specific, statutory basis. Once taken, it must be adhered to in private and public transactions alike and enforced by the insurance regulator. This is, in effect, a standard for conduct that by design transcends and preempts economic considerations. Because it is not applied without economic cost, however, insurance regulators and the industry should participate in and inform legislative deliberations that would ban the, use of particular rating variables as discriminatory.

And then, more recently, we started to talk about personalization, as in Barry and Charpentier (2020). And next week, we will start talking about predictive modeling, and machine learning.

# Want to say one thing and the exact oppositive with strong confidence ?

No need to do politics. Just take a statistical course. And I do not talk about misinterpretation of statistics, but I talk about the mathematical foundations of statistical tests.
Consider the following parametric test, with a one-dimensional parameter:  versus , for some (fixed) . A standard way of doing such a test is to consider an rejection region . The test works as follows: consider a sample ,

• if , then we accept
• if , the we reject

For instance, consider the case of a Bernoulli sample, with probability . The standard idea is to define

The rejection region is then based on statistic ,

• if , then we accept
• if , the we reject

where threshold  is taken so that the probability to make a first type error is (say 5%) using the Gaussian approximation for z. Here

Thus, the acceptation region is then the green area below, while the rejection region is the red one, for .

Consider now the exact opposite test (with the same ), versus . Here, we use the same statistics, and the test is

• if , then we accept
• if , the we reject

where now

Thus, now, the acceptation region is then the green area below, while the rejection region is the red one.

So if we summarize what we just said,

• in the region on the left below, both test agree that
• in the region on the right below, both test agree that
• and in the region in blue, in the middle, the two tests disagree (one claims that , and the other one that )

Here is the evolution of the region as a function of  (the size of the sample) when the sample frequency is 20%. With a small sample size, we can hardly say anything.

n=seq(1,100)
p=0.2
x1=p+qnorm(.95)*sqrt(p*(1-p)/n)
x2=p+qnorm(.05)*sqrt(p*(1-p)/n)
plot(n,x1,type="l",ylim=c(0,1))
polygon(c(n,rev(n)),c(x1,rev(x2)),col="light blue",border=NA)
lines(n,x1,lwd=2,col="red")
lines(n,x2,lwd=2,col="red")

One might say that those bounds are based on a Gaussian approximation which is not correct when  is too small. So we can compute exact bounds,
y1=qbinom(.95,size=n,prob=p)/n
y2=qbinom(.05,size=n,prob=p)/n
polygon(c(n,rev(n)),c(y1,rev(y2)),col="blue",border=NA)
lines(n,y1,lwd=2,col="red")
lines(n,y2,lwd=2,col="red")

and we get

This is what we can observe if we use R statistical procedures, either the asymptotic one,

> prop.test(2,10,.5,alternative="less")

1-sample proportions test with continuity correction

data:  2 out of 10, null probability 0.5
X-squared = 2.5, df = 1, p-value = 0.05692
alternative hypothesis: true p is less than 0.5
95 percent confidence interval:
0.0000000 0.5100219
sample estimates:
p
0.2

> prop.test(2,10,.5,alternative="greater")

1-sample proportions test with continuity correction

data:  2 out of 10, null probability 0.5
X-squared = 2.5, df = 1, p-value = 0.943
alternative hypothesis: true p is greater than 0.5
95 percent confidence interval:
0.04368507 1.00000000
sample estimates:
p
0.2

or a more accurate one

> binom.test(2,10,.5,alternative="less")

Exact binomial test

data:  2 and 10
number of successes = 2, number of trials = 10, p-value = 0.05469
alternative hypothesis: true probability of success is less than 0.5
95 percent confidence interval:
0.0000000 0.5069013
sample estimates:
probability of success
0.2

> binom.test(2,10,.5,alternative="greater")

Exact binomial test

data:  2 and 10
number of successes = 2, number of trials = 10, p-value = 0.9893
alternative hypothesis: true probability of success is greater than 0.5
95 percent confidence interval:
0.03677144 1.00000000
sample estimates:
probability of success
0.2

Here, when the sample frequency is 20% and  is equal to 10, we accept at the same time that theta is higher than 50% and lower than 50%.
And obviously it is not only a theoretical problem: it has obviously some strong implications. This morning, a good friend mentioned a post published some months ago, online here, about discrimination, and the lack of women with academic positions in mathematics, in France. As claimed by the author of the post“A Paris VI, meilleure université française selon son président, sur 11 postes de maitres de conférences, 5 filles classées premières. Il y a donc des filles excellentes ? A Toulouse, sur 4 postes, 2 filles premières. Parité parfaite. Mais à côté de cela, Bordeaux, 4 postes, 0 fille première. Littoral, 3 postes, 0 fille, Nice, 5 postes, 0 fille, Rennes, 7 postes, 0 fille…”.
Consider the latter one: in Rennes, out of 7 people hired last year, no woman. So in some sense, it looks obvious that there is some kind of discrimination ! Zero out of seven ! Well, if we consider the fact that around 30% of PhD thesis in mathematics were defended by women those years, we can also try to see is there if no “positive discrimination“, i.e. test  where theta is the probability to hire a woman (just to be a little bit provocative).

> prop.test(0,7,.3,alternative="less")

1-sample proportions test with continuity correction

data:  0 out of 7, null probability 0.3
X-squared = 1.7415, df = 1, p-value = 0.09347
alternative hypothesis: true p is less than 0.3
95 percent confidence interval:
0.0000000 0.3719021
sample estimates:
p
0

Warning message:
In prop.test(0, 7, 0.3, alternative = "less") :
Chi-squared approximation may be incorrect
> binom.test(0,7,.3,alternative="less")

Exact binomial test

data:  0 and 7
number of successes = 0, number of trials = 7, p-value = 0.08235
alternative hypothesis: true probability of success is less than 0.3
95 percent confidence interval:
0.0000000 0.3481637
sample estimates:
probability of success
0

With no woman hired that year, we can still pretend that there was some kind of “positive discrimination“. An note that we do accept – with more confidence – the assumption of “positive discrimination” if we look at all universities together,

> prop.test(5+2,11+4+4+3+5+7,.3,alternative="less")

1-sample proportions test with continuity correction

data:  5 + 2 out of 11 + 4 + 4 + 3 + 5 + 7, null probability 0.3
X-squared = 1.021, df = 1, p-value = 0.1561
alternative hypothesis: true p is less than 0.3
95 percent confidence interval:
0.0000000 0.3556254
sample estimates:
p
0.2058824

> binom.test(5+2,11+4+4+3+5+7,.3,alternative="less")

Exact binomial test

data:  5 + 2 and 11 + 4 + 4 + 3 + 5 + 7
number of successes = 7, number of trials = 34, p-value = 0.1558
alternative hypothesis: true probability of success is less than 0.3
95 percent confidence interval:
0.0000000 0.3521612
sample estimates:
probability of success
0.2058824`

So obviously, with small sample, almost anything can be claimed !

# Ramadan, entre le sexe et la faim…

Dans mon précédant billet (ici) je volais un dessin de Martin Vidberg qui parlait de google et de statistiques… Google trend est un outil magique pour noter des tendances et faire des statistiques simples et marrantes. Par exemple, puisque le ramadan touche à sa fin, on peut regarder ce qui motivent les algériens pendant le ramadan. Manifestement, le sexe n’est pas une priorité, que ce soit en anglais,

ou en français,

De manière générale, le mot clé le plus tapé sous google a tendance à se faire tout petit pendant la période du ramadan, alias porno,

En fait, on cherche un peu moins les femmes sur le net,

Par contre, visiblement, on a faim, et on veut bien manger: cuisine explose pendant le ramadan,

On peut aussi regarder les autres pays à forte population musulmane, et observer des tendances similaires, comme le Maroc,

les Émirats Arabes Unis,

ou encore le Pakistan,

(dans ce dernier cas, on observe un fort retour du mot clé sexune fois le ramadan passé… une espèce de manque virtuel). Bref, avec Google, on peut vraiment observer des choses intéressantes… non ?