Cross-Domain Behavioral Credit Modeling: transferability from private to central data

This paper introduces a credit risk rating model for credit risk assessment in quantitative finance, aiming to categorize borrowers based on their behavioral data. The model is trained on data from Experian, a widely recognized credit bureau, to effectively identify instances of loan defaults among bank customers. Employing state-of-the-art statistical and machine learning techniques ensures the model's predictive accuracy. Furthermore, we assess the model's transferability by testing it on behavioral data from the Bank of Italy, demonstrating its potential applicability across diverse datasets during prediction. This study highlights the benefits of incorporating external behavioral data to improve credit risk assessment in financial institutions.Comment: 25 pages, 15 figure

A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream

The problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy is addressed. This study is based on a large set of measurements collected from real pebbles, traced along the stream through radio-frequency identification tags after precipitation events. Two classes of data-driven models based on machine learning and functional geostatistics approaches are proposed and evaluated to predict the probability of movement of single pebbles within the stream. The first class built upon gradient-boosting decision trees allows one to estimate the probability of movement of a pebble based on the pebbles' geometrical features, river flow rate, location, and subdomain types. The second class is built upon functional kriging, a recent geostatistical technique that allows one to predict a functional profile-that is, the movement probability of a pebble, as a function of the pebbles' geometrical features or the stream's flow rate-at unsampled locations in the study area. Although grounded in different perspectives, both models aim to account for two main sources of uncertainty, namely, (1) the complexity of a river's morphological structure and (2) the highly nonlinear dependence between probability of movement, pebble size and shape, and the stream's flow rate. The performance of the two methods is extensively compared in terms of classification accuracy. The analyses show that despite the different perspectives, the overall performance is adequate and consistent, which suggests that both approaches can provide modeling frameworks for sediment transport. These data-driven approaches are also compared with physics-based ones that are classically used in the hydrological literature. Finally, the use of the developed models in a bottom-up strategy, which starts with the prediction/classification of a single pebble and then integrates the results into a forecast of the grain-size distribution of mobilized sediments, is discussed

Technique for rigidity determination of the materials for ossicles prostheses of human middle ear

The theoretical analysis of a technique for rigidity determination of the materials for ossicles prostheses of human middle ear has been carried out based on the measurement of the acoustical velocity. This paper presents the results of rigidity modulus measurements for the ossicles prostheses made from the materials of different origin, based on which the known materials have been identified and a novel one has been suggested, namely foam polyurethane (PU foam-3). It has been recommended to utilize the polymer material in the case of substitution of the entire ossicles chain and the bioactive ceramics in the case of partial substitution