Forecasting recovery rates on non-performing loans with machine learning

We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process - prior to the portfolio's sale to the debt collector - are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential

Reconstrucción del evento eruptivo asociado al emplazamiento del flujo piroclástico El Refugio hace 13 ka, volcán Nevado de Toluca (México)

El Nevado de Toluca es un volcán activo en estado de quietud, localizado en el sector central del Cinturón Volcánico Transmexicano, 80 km al suroeste de Ciudad de México. Su formación ha sido caracterizada por una etapa efusiva inicial (entre 2.6 y 1.15 Ma), de composición andesítico-dacítica y una etapa explosiva más reciente (desde los 42 ka) que se manifestó con la alternancia de cinco erupciones plinianas (42, 36, 21.7, 12.1 y 10.5 ka) y de por lo menos cinco destrucciones de domos (37, 32, 28, 26 y 13 ka) asociados al emplazamiento de fl ujos de bloques y ceniza alrededor del volcán. Hace aproximadamente 13 ka ocurrió el evento más reciente de destrucción de domo, con el emplazamiento en el sector N-NE de un fl ujo piroclástico, aquí denominado fl ujo El Refugio, con un volumen de 0.11 km3. El depósito está constituido por dos facies de fl ujo: facies central, hasta 10 m de espesor, que consiste de hasta cinco unidades de fl ujo con clastos de varios decímetros de diámetro en una matriz arenosa; facies lateral, hasta 4 m de espesor, que consiste de una unidad masiva de material arenoso. En la base de la secuencia afl ora un depósito de oleada piroclástica de hasta 30 cm de espesor. Fragmentos de dacita representan el componente principal del depósito, con distinto grado de vesicularidad y con una asociación mineralógica de Pl-Hbl-Opx. Con base en las características estratigráfi cas, petrográfi cas y de la textura de los componentes juveniles, se pudo determinar que la extrusión del domo fue un proceso muy rápido y que su destrucción fue acompañada por una componente explosiva. El proceso magmático que dio inicio a la actividad fue debido a un sobrecalentamiento de la cámara magmática que promovió un proceso de 'self-mixing' con movimientos convectivos que llevaron a la cristalización y sobrepresión del reservorio. Finalmente, poder determinar una componente explosiva asociada a la destrucción de domos somitales en el Nevado de Toluca, pone en evidencia el alto peligro que este tipo de actividad podría representar en un futuro para las poblaciones aledañas

Forecasting recovery rates on non-performing loans with machine learning

We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process - prior to the portfolio's sale to the debt collector - are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential

Forecasting recovery rates on non-performing loans with machine learning

We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process - prior to the portfolio's sale to the debt collector - are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential

Beyond virtual museums: adopting serious games and extended reality (xr) for user-centred cultural experiences

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-37191-3_15© 2020, Springer Nature Switzerland AG. Since museums, heritage sites and archives are important for the preservation of our cultural heritage; recently there has been an attempt to promote better absorption of cultural knowledge by involving the learners in the process

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure–Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure–Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure–Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure–Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it

2020 Dataset on local gambling regulations in Italy

The dataset provides the complete enumeration of gambling policies implemented by Italian Municipalities between 2003 and 2021. The dataset comprises information on all municipalities existing in 2017 and following years (thus considering also merging). The following variables are available: Municipality ISTAT Code (ID), Municipality Name (Name) Province, Region, Researcher, and a series of variables identifying the number and the type of rulings adopted ('regolamento', 'ordinanza' and 'delibera'). The rulings are distinguished between identified and downloaded or only identified (because the document is no longer available). Additional variables describe municipal activism with other administrative acts (Anti-gambling Manifesto, events, projects or tax reductions). Overall, the dataset comprises 8031 units