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This paper analyses the impact of the gender of the loan officer-borrower pair on the loan-size, credit availability and time spent in managing the credit application. By using 3,020 lending transactions from Ecuador during 2016–2019, we document that the loan officer-borrower pair gender drives differences in the microcredit portfolio management. We find that female loan officers grant smaller microcredits to both male and female borrowers. However, the loan-size increases when matching the loan officer-borrower gender. We also demonstrate that female loan officers have greater loan approval rates, mainly in the segment of higher loan-sizes granted to male clients. Finally, our results show that the microcredits that have the fastest evaluation process are those that have a lower loan-size and are granted by female loan officers to female borrowers. Our findings have practical implications for the analysis of the credit availability for entrepreneurs as well as the loan portfolio management.

Abstract Disruptive innovations caused by FinTech (i.e., technology-assisted customized financial services) have brought digital peer-to-peer (P2P) payments to the fore. In this challenging environment and based on theories about customer behavior in response to technological innovations, this paper identifies the drivers of consumer adoption of mobile P2P payments and develops a machine learning model to predict the use of this thriving payment option. To do so, we use a unique data set with information from 701 participants (observations) who completed a questionnaire about the adoption of Bizum, a leading mobile P2P platform worldwide. The respondent profile was the average Spanish citizen within the framework of European culture and lifestyle. We document (in this order of priority) the usefulness of mobile P2P payments, influence of peers and other social groups such as friends, family, and colleagues on individual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (potential) users of mobile P2P payments. We also find that nonparametric approaches based on machine learning algorithms outperform traditional parametric methods. Finally, our results show that feature selection based on random forest, such as the Boruta procedure, as a preprocessing technique substantially increases prediction performance while reducing noise, redundancy of the resulting model, and computational costs. The main limitation of this research is that it only has a place within the sociocultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the effects of the institutional environment on the adoption of mobile P2P payments.

Fundamental knowledge on intrinsic plutonium colloids is important for the prediction of plutonium behaviour in the geosphere and in engineered systems. The first synthetic route to obtain salt-free intrinsic plutonium colloids by ultrasonic treatment of PuO2 suspensions in pure water is reported. Kinetics showed that both chemical and mechanical effects of ultrasound contribute to the mechanism of Pu colloid formation. In the first stage, fragmentation of initial PuO2 particles provides larger surface contact between cavitation bubbles and solids. Furthermore, hydrogen formed during sonochemical water splitting enables reduction of Pu(IV) to more soluble Pu(III), which then re-oxidizes yielding Pu(IV) colloid. A comparative study of nanostructured PuO2 and Pu colloids produced by sonochemical and hydrolytic methods, has been conducted using HRTEM, Pu LIII-edge XAS, and O K-edge NEXAFS/STXM. Characterization of Pu colloids revealed a correlation between the number of Pu-O and Pu-Pu contacts and the atomic surface-to-volume ratio of the PuO2 nanoparticles. NEXAFS indicated that oxygen state in hydrolytic Pu colloid is influenced by hydrolysed Pu(IV) species to a greater extent than in sonochemical PuO2 nanoparticles. In general, hydrolytic and sonochemical Pu colloids can be described as core-shell nanoparticles composed of quasi-stoichiometric PuO2 cores and hydrolyzed Pu(IV) moieties at the surface shell.

The article is dedicated to the solution of the relevant issue, concerning management of enterprise`s financial sustainability. Financial sustainability assessment is one of the most important principles of enterprise`s activity, which provides information about financial capabilities of the company at the time of its evaluation and for the future. It is a requirement for the enterprise activity, which provides a high level of competitiveness, efficiency and intensity. Thus, the restoration and strengthening of financial sustainability is a priority task for the enterprise, a basic precondition for its effective functioning. The purpose of the research is to analyze financial sustainability and to improve the methods and approaches of its evaluation. The following methods were used in this research: financial and economic analysis, economic and mathematical modeling, analysis and synthesis, comparison. The main results of the study are following: the theoretical and essential characteristics of enterprise financial sustainability has been determined; the financial status of PJSC “Turboatom” has been analyzed; the taxonomic index of financial sustainability has been calculated and the forecast of its significance has been made, the approaches to increase enterprise financial sustainability have been proposed.

La importancia de las micro-entities como generadoras de empleo y propulsoras de la actividad económica conlleva, unida a sus mayores tasas de quiebra y a su dificultad para acceder a las fuentes de financiación, la necesidad de diseñar métodos apropiados que anticipen sus quiebras. Con este fin, en este trabajo se desarrolla un modelo híbrido mediante la combinación de enfoques paramétricos y no paramétricos para la detección de sus quiebras. Para ello, se seleccionan las variables con mayor poder predictivo para detectar la quiebra mediante un modelo híbrido de regresión logística (LR) y árboles de regresión y clasificación (CART). Nuestros resultados muestran que este modelo híbrido obtiene una mejor performance que aquellos modelos implementados de forma aislada, además de tener una más fácil interpretación y una convergencia más rápida. Por otra parte, se constata la conveniencia de la introducción de variables no financieras y macroeconómicas que complementen a la información proporcionada por los ratios financieros para la predicción de la quiebra de las micro-entities, lo cual está en línea con las características propias e idiosincrasia de este tamaño empresarial recientemente definido por la Comisión Europea. ------------------------------------ The importance of micro-entities due to their generation of employment and propelling economic activity, together with the fact of their particularities, implies the need to design appropriate methods that anticipate their bankruptcies. For that purpose, a hybrid model by combining parametric and nonparametric approaches is developed in this paper. First, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, a non-parametric method, namely regression trees and classification (CART), is then applied to companies classified as "bankruptcy" or "non-bankruptcy". Our results show that this model provides a better result than when it is implemented in isolation, which joins its easier interpretation and faster convergence. Moreover, we demonstrate that the introduction of non-financial and macroeconomic variables complement the financial ratios for bankruptcy prediction. Findings are based on a data set of micro-entities (MEs), as recently defined by the European Union.

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

The hydrothermal decomposition of actinide(IV) oxalates (An= Th, U, Pu) at temperatures between 95 and 250 °C is shown to lead to the production of highly crystalline, reactive actinide oxide nanocrystals (NCs). This aqueous process proved to be quantitative, reproducible and fast (depending on temperature). The NCs obtained were characterised by X-ray diffraction and TEM showing their size to be smaller than 15 nm. Attempts to extend this general approach towards transition metal or lanthanide oxalates failed in the 95–250 °C temperature range. The hydrothermal decomposition of actinide oxalates is therefore a clean, flexible and powerful approach towards NCs of AnO2 with possible scale-up potential.

Micro enterprises (MEs) represent over 75 % of all enterprises in the EU, accounting for over 30 % of employment. However, since the onset of the economic crisis in 2008, this business segment has suffered high rates of bankruptcies and business closures, destroying many jobs. The construction of models that anticipate insolvency to allow sufficient time to take appropriate action is important to avoid bankruptcy of the MEs. However, it is difficult to obtain complete and relevant information for MEs, making it very difficult to be a good fit of the models for predicting corporate failure for this size of company. Applying Rough Sets technique as a method for pre - processing of the data, in the present study, we order the variables that best discriminate between solvent / insolvent in order to increase efficiency in predicting insolvency MEs. Additionally, we provide an application of the technique to family- MEs. Throughout this process, our results highlight the importance of considering non-financial variables to predict insolvency of MEs.

Despite the leading role that micro-entrepreneurship plays in economic development, and the high failure rate of microenterprise start-ups in their early years, very few studies have designed financial distress models to detect the financial problems of micro-entrepreneurs. Moreover, due to a lack of research, nothing is known about whether non-financial information and nonparametric statistical techniques improve the predictive capacity of these models. Therefore, this paper provides an innovative financial distress model specifically designed for microenterprise startups via support vector machines (SVMs) that employs financial, non-financial, and macroeconomic variables. Based on a sample of almost 5,500 micro- entrepreneurs from a Peruvian Microfinance Institution (MFI), our findings show that the introduction of non-financial information related to the zone in which the entrepreneurs live and situate their business, the duration of the MFI-entrepreneur relationship, the number of loans granted by the MFI in the last year, the loan destination, and the opinion of experts on the probability that microenterprise start-ups may experience financial problems, significantly increases the accuracy performance of our financial distress model. Furthermore, the results reveal that the models that use SVMs outperform those which employ traditional logistic regression (LR) analysis.