Ghana welcomes Pope's apology over Catholic Church's role in slavery Ghana has welcomed Pope Leo XIV's apology for the Catholic Church's historic role in slavery, describing it as an act of moral courage that was important in the global pursuit of truth, human dignity and justice. The Pope issued the clearest apology yet for the Church's involvement in legitimising slavery and its delay in condemning it for centuries. The apology was published on Monday in the Pope's first major teaching document of his papacy, which also focused on the dangers of artificial intelligence (AI) . Ghana was a major hub for the transatlantic slave trade when millions of people were captured and loaded on to ships, never to return home. Between the 16th and 19th Centuries, 12-15 million Africans were shipped to the Caribbean, with about two million dying during the journey.

Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be incorporated into actuarial models for Motor Third Party Liability (MTPL) claim prediction under such constraints. Using the BeMTPL97 dataset, we adopt a zone-level modeling framework and evaluate predictive performance on unseen postcodes. Geographic information is introduced through two channels: environmental indicators from OpenStreetMap and CORINE Land Cover, and orthoimagery released by the Belgian National Geographic Institute for academic use. We evaluate the predictive contribution of coordinates, environmental features, and image embeddings across three baseline models: generalized linear models (GLMs), regularized GLMs, and gradient-boosted trees, while raw imagery is modeled using convolutional neural networks. Our results show that augmenting actuarial variables with constructed geographic information improves accuracy. Across experiments, both linear and tree-based models benefit most from combining coordinates with environmental features extracted at 5 km scale, while smaller neighborhoods also improve baseline specifications. Generally, image embeddings do not improve performance when environmental features are available; however, when such features are absent, pretrained vision-transformer embeddings enhance accuracy and stability for regularized GLMs. Our results show that the predictive value of geographic information in zone-level MTPL frequency models depends less on model complexity than on how geography is represented, and illustrate that geographic context can be incorporated despite limited individual-level spatial information.

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