The insurance company uses different factors to classify the policyholders. In this study, we apply several machine learning models such as nearest neighbour and logistic regression to the Actuarial Challenge dataset used by Qazvini (2019) to classify liability insurance policies into two groups: 1 - policies with claims and 2 - policies without claims. The applications of Machine Learning (ML) models and Artificial Intelligence (AI) in areas such as medical diagnosis, economics, banking, fraud detection, agriculture, etc, have been known for quite a number of years. ML models have changed these industries remarkably. However, despite their high predictive power and their capability to identify nonlinear transformations and interactions between variables, they are slowly being introduced into the insurance industry and actuarial fields.

In mid-February, the Centre Hospitalier de Bigorre in Tarbes (Hautes-Pyrénées) organized the inauguration of an Artificial Intelligence module for digestive endoscopy, in order to optimize colorectal cancer screening: CAD EYE by Fujifilm. The hospital's endoscopy department has already been using it for a year and is witnessing the benefits of such a technological innovation for the care of patients in the Hautes Pyrénées. Colorectal cancer is the 3rd most common cancer after lung cancer and breast cancer, and the second most common cause of death by cancer after lung cancer. However, if detected at an early stage, colorectal cancer can be cured in 9 out of 10 cases, which is possible with colonoscopy (lower digestive endoscopy) for the detection of colon tumors. On the other hand, an accurate endoscopic diagnosis of colon polyps could reduce the number of unnecessary polypectomies. In March 2021, the endoscopy department was able to acquire the Fujifilm CAD EYE box equipped with artificial intelligence thanks to the financing and the important mobilization of the League against Cancer of the Hautes Pyrénées, the Departmental Council, the Lions Club and the Rotary Club, organizers of the Maxi Loto of Lourdes, for the benefit of cancer research.

Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.