Macro-Economic and Bank-Specific Determinants of Credit Risk in Commercial Banks

Inadequate credit risk assessment procedures may have a significant negative influence on a financial institution's operational performance, perhaps leading to liquidity concerns.  It is hypothesized that different factors such as macroeconomic, and bank-specific factors affect the credit risk in financial institutions. The objective of this study is to check those factors responsible for credit risk. The data came from WDI and Bankscope databases. The data is balanced panel data of 106 private and state-owned commercial banks for 6 years (n=106, t=6). This study used Fixed Effect (FE), and Random Effect (RE) models. The results suggest that if inflation, interest rate, unemployment increase, the credit risk of commercial banks increases. The results also suggest that if GDP growth, efficiency, and bank size increase, the credit risk become minimized. Additionally, the credit risk is lower in private banks than in state-owned banks. The findings of this research, however, do not support the hypotheses that exchange rate and regulatory capitals influence credit risk

The regeneration of a weak acid action exchanger with an aequeous solution of sulphurdioxide

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, in fu lfilm e n t o f the requirements for the degree of Master of Science in Engineering by Paul R a iter, B.Sc. (Eng,) Johannesburg September

An implementation of sharpe’s capital asset pricing model

This paper implements computationally Sharpe (1964) Capital Asset Pricing Model (CAPM) in order to provide a script that collects data, calculates the parameters of CAPM and tests the statistical significance of those parameters for any asset traded in Brazilian Stock Exchange (B3). The model is estimated using Brazilian’s interbank interest rate (CDI) as the risk free return and B3 stock market index (Ibovespa) return as a proxy for market return. We implemented the CAPM by using Python programming language and tested the program operation for a subset of B3 stock market assets. The results has shown the application was able to attend all functional requirements and, in addition, it is amenable to be developed to provide new features such as parameters of international capital asset pricing model as stated in Frankel (1982).Esse artigo implementa computacionalmente Sharpe (1964) Capital Asset Pricing Model (CAPM) com o objetivo de prover uma aplicação que coleta dados, calcula os parâmetros do CAPM e testa a significância estatística desses parâmetros para qualquer ação negociada na bolsa de valores brasileira (B3). O modelo é estimado utilizando o Certificado de Depósito Interbancário como a taxa livre de risco e o Ibovespa como uma proxy para o retorno de mercado. Nós implementamos o CAPM utilizando a linguagem de programação Python e testamos a operação do programa para um conjunto de ações da B3. Os resultados mostraram que a aplicação foi capaz de atender todos os requerimentos funcionais e, em adição, é passível de desenvolvimento para prover novas funcionalidades, como parâmetros do CAPM internacional como exposto em Frankel (1982)

Applying Supervised Machine Learning Algorithms for Fraud Detection in Anti-Money Laundering

As international money transfers become more automated, it becomes easier for criminals to transfer money across borders in a fraction of a second, while it also becomes easier for regulators to inspect and monitor international money mobility and identify unusual patterns of money movement. Machine learning algorithms may be a useful addition to the current money laundering detection issues. This research empirically tested four machine learning algorithms (Logistic regression, SVM, Random Forest, and ANN) using a synthetic dataset that closely matches regular transaction behavior. After observing the performance of different algorithms, it can be stated that the Random Forest technique, when compared to the other techniques, provides the best accuracy. The least accurate approach was the Artificial Neural Network (ANN)

Causas de morte em pinguins-de-Magalhães (Spheniscus magellanicus) no sul do Brasil

Os pinguins são aves marinhas com comportamento pelágico que atualmente possuem um total de 18 espécies reconhecidas. Dentre essas espécies, o pinguim-de-Magalhães (Spheniscus magellanicus) é a que ocorre com maior frequência no litoral brasileiro. Essas aves comumente são indivíduos jovens em seu primeiro ano de vida. Este estudo teve como objetivo descrever as causas de mortalidade e os achados patológicos em pinguins-de-Magalhães no sul do Brasil. Entre janeiro de 2011 a dezembro de 2021 foram analisados os registros de 164 pinguins-de-Magalhães recebidos para necropsia no Setor de Patologia Veterinária da Universidade Federal do Rio Grande do Sul, dos quais 41,5% (68/164) eram de vida livre, 38,4% (63/164) em reabilitação, 14,6% (24/164) cativos, 4,9% (8/164) em reabilitação por tempo indeterminado e 0,6% (1/164) de origem desconhecida. Cento e vinte e sete (77,4%) dos casos analisados tiveram diagnósticos conclusivos e 37 (22,6%) casos foram inconclusivos. Dos casos conclusivos, doenças não-infecciosas foram a causa de morte mais frequente (104/164; 63,4%), seguido de doenças infeciosas/inflamatórias (23/164; 14,0%). Dentro das causas não-infeciosas, o diagnóstico mais frequente foi inanição (49/104; 47,1%), seguido de suspeita de afogamento (27/104; 26,0%), hipotermia (25/104; 24,0%), trauma (2/104; 1,9%) e obstrução intestinal (1/104; 1,0%). Para as causas infecciosas/inflamatórias, os diagnósticos mais frequentes foram malária aviária (9/23; 39,1%), aspergilose (6/23; 26,0%), outras infecções fúngicas (4/23; 17,4%), infecção bacteriana (2/23; 8,7%), meningoencefalite protozoária (1/23; 4,4%) e aerossaculite granulomatosa de origem desconhecida (1/23; 4,4%). Grande parte dos pinguins apresentavam lesões secundárias não relacionadas à causa da morte, incluindo parasitose gastrointestinal [nematodíase (133/164; 81,1%), cestodíase (47/164; 28,7%) e trematodíase (25/164; 15,2%)], lesões cutâneas [abrasão, eritema, ulceração e laceração (19/164; 11,6%) e pododermatite (9/164; 5,5%)] e edema pulmonar (13/164; 7.9%). Os resultados obtidos são importantes para a avaliação da mortalidade de pinguins-de-Magalhães em vida livre e mantidos sob cuidados humanos, uma vez que auxiliam na compreensão dos processos patológicos que afetam a espécie.Penguins are seabirds with pelagic behavior that currently have a total of 18 recognized species. Among these species, the Magellanic penguin (Spheniscus magellanicus) is the one that occurs most frequently on the Brazilian coast. These birds are usually young individuals in their first year of life. This study aimed to describe the causes of death and the pathological findings in Magellanic penguins in Southern Brazil. From January 2011 to December 2021, the records of 164 Magellanic penguins, of which 41.5% (68/164) were free-ranging, 38.4% (63/164) in rehabilitation, 14.6% (24/164) captive, 4.9% (8/164) of unknown time rehabilitation, and 0.6% (1/164) of undetermined origin, received for necropsy at the Department of Veterinary Pathology of the Universidade Federal do Rio Grande do Sul were analyzed. One hundred twenty-seven (77.4%) of the cases had conclusive diagnoses and 37 (22.6%) cases were undetermined diagnoses. Of conclusive cases, non-infectious diseases were the most frequent cause of death (104/164; 63.4%), followed by infectious/inflammatory diseases (23/164; 14.0%). Among non-infectious causes, the most frequent diagnosis was starvation (49/104; 47.1%), followed by suspected drowning (27/104; 26.0%), hypothermia (25/104; 24.0%), trauma (2/104; 1.9%) and intestinal obstruction (1/104; 1.0%). For infectious/inflammatory causes, the diagnosis of avian malaria was the most frequent (9/23; 39.1%), followed by aspergillosis (6/23; 26.0%), other mycotic infections (4/23; 17.4%), bacterial infection (2/23; 8.7%), protozoal meningoencephalitis (1/23; 4.4%) and granulomatous air sacculitis of unknown origin (1/23; 4.4%). In addition, most penguins had secondary pathological findings unrelated with the cause of death, mainly gastrointestinal parasitosis (nematodiasis [133/164; 81.1%], cestodiasis [47/164; 28.7%] and trematodiasis [25/164; 15.2%]), cutaneous lesions (abrasion, erythema, ulceration and laceration [19/164; 11.6%] and bumble foot [9/164; 5.5%]) and pulmonary edema (13/164; 7.9%). These results are important to evaluate mortality in Magellanic penguins’ populations in the wild and under human care, as they help to understand the diseases that affect the species

Variación lingüística e identidad

En este trabajo repasamos algunas de las iniciativas vinculadas con el llamado lenguaje inclusivo. Refutaremos algunas de las afirmaciones normativas producidas por la Real Academia Española y trataremos de mostrar que se trata de un caso de variación lingüística impulsada por la necesidad de autoidentificación para un grupo social

Methodology of actors analysis and modeling of the amounts of solid municipal waste generation within tourist destinations

The article presents the results of the study of factors influencing the amount of waste generated within tourist destinations. By means of dispersion and regression analysis functional dependencies were established. They allowed determining the connection between the factors and amounts of waste generation within tourist destinations. The established functional dependencies allowed forecasting the amount of waste generation within the territories of tourist activities. The authors determined that the factors influencing the amount of waste generated within tourist destinations are the number of tourists and excursionists, GDP per capita, number of population. 3D surfaces built in the program Surfer made it possible to assess the influence of different combinations of factors on the process of waste generation. A mathematical model of the process of waste generation within the territories of tourist destinations was built in order to help to create efficient waste management systems within the territories of tourism developmen

Segmentation of Bank Consumers for Artificial Intelligence Marketing

Banks can offer more personalized products and services by using segmentation solutions. By gaining a deeper understanding of client characteristics, marketers can Choose the appropriate promotional content to deliver, choose the right marketing channels for the target market, identify new and profitable market sectors, and introduce new products and services. Artificial intelligence marketing uses artificial intelligence concepts and models such as machine learning and Bayesian networks. Cluster analysis is a machine learning method for classifying entities into groups that are homogenous in terms of observable characteristics. This study included K-means cluster analysis, Elbow, and silhouette approaches to segment the data for cardholders of various banks. According to the results from Elbow and the silhouette, the ideal number of clusters seems to be five. Based on their income and shopping frequencies, which are supposedly the greatest attributes to establish the segments of the customers, this research identified five distinct consumer segments: Savers, General, Targets, and Big spenders. This research recommends leveraging machine learning techniques to devise various marketing tactics and policies to maximize the bank’s efficiency, customer satisfaction, and quality of services