The Impact on Women on the Removal of Gender as a Rating Variable in Motor-Vehicle Insurance
Notice bibliographique
Résumé
Insurers use actuarial statistics as rating variables to differentiate and distinguish for the purposes of risk classification. They justify their use of actuarial statistics due to its accuracy as a predictor of risk. South African motor-vehicle insurers use gender, inter alia, as a rating variable to classify risks into certain classes and to determine insurance premiums. Depending upon whether the insured is male or female, it could have a significant impact on the cost of his or her premium. Women drivers pay less for motor-vehicle insurance because actuarial statistics indicate that women are more careful drivers and are involved in 20 per cent fewer accidents than men. Men pay higher premiums because the statistics indicate that they are less responsible drivers than women. Should a South African court decide that the use of gender as a motor-vehicle insurance rating variable is unfair discrimination, this would benefit male drivers, as it would lower their premium. Women, on the other hand, would be disadvantaged as they would be required to pay higher premiums to subsidise men. The article examines the impact that the removal of gender as a rating variable in motor-vehicle insurance would have on women, and asks if the effects thereof would influence a South African Court’s decision in determining if the use of gender as a rating variable amounts to unfair discrimination. The article first considers the findings of American and Canadian Courts in determining this same issue and then considers South African equality legislation, particularly the Promotion of Equality and Prevention of Unfair Discrimination Act 4 of 2000 (“the Equality Act”). Thereafter, the article provides recommendations for a South African Court. As the Equality Act indicates that the discriminatory insurance practice of placing a disadvantage or advantage on persons based inter alia on their gender may possibly be unfair, it is suggested that South African insurers would have to consider alternative methods of risk assessment. In the light of the American and the Canadian case law, the article suggests that there should be a change of approach to insurance risk assessment. Rather than using gender as a rating variable the insurer could assess the risk of the individual insured, using appropriate, neutral rating variables suited to the particular circumstances of the insured. This would require a much more intensive and individualised risk evaluation and would require the insurer to “tailor-make” insurance for each individual. It is submitted that such an approach would give effect to the right to equality by disallowing the use of gender as a rating variable without producing the undesirable consequence that women drivers would have to subsidise men.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,004 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».