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Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of the explainability tools proposed in the last few years, such as the SHAP values. In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending. The comparison reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability. More precisely, the SHAP values reveal that machine learning algorithms can reflect dispersion, nonlinearity and structural breaks in the relationships between each feature and the target variable. Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent. Such models provide the trust that the industry, regulators and end-users demand in P2P lending and may lead to a wider adoption of machine learning in this and other risk assessment applications where explainability is required.

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Purpose This paper aims to find out if different exporter types dominate among matched mature Spanish and Estonian firms and whether these types are associated with specific export growth/decline patterns. Design/methodology/approach This study is based on firm-level data from the Estonian Business Register’s database of annual financial reports and SEPI Foundation’s survey on Spanish firms’ business strategies. From both countries, 242 firms were included and the period 2009-2013 was chosen. Findings Committed exporters (with 75 per cent or higher export shares) dominated in Estonia and experimental exporters (with export shares mostly below 10 per cent) in Spain. While in Estonia, the most frequent export growth/decline pattern encompassed four consecutive growth years, in Spain, it had two consecutive growth years and then two decline years. Spanish firms’ export growth/decline patterns were more random: 12 patterns of 16 fell within the range of a random walk assumption, while in Estonia, only 5 patterns were within the range. Contingency existed between exporter types and export growth/decline patterns only for the whole sample. Originality/value This paper studies if committed/aggressive/active exporters experience more export fluctuations than passive/experimental exporters, and how random export growth/decline patterns are.

This study aims to find out how useful managers' past general and export experience is in predicting whether young manufacturing firms become fast internationalizers. Extant literature about the role of managerial experience in determining young firms' internationalization type is scant. This paper fills this gap by providing systematic evidence on which kinds of general and export experience can be used for accurate predictions of two firm types: born globals and general fast internationalizers. Our dataset encompasses information about managerial experience of the whole population of young Estonian manufacturing firms. Based on using four different prediction methods (logistic regression, rough sets, decision tree, neural networks) and a large variety of variables reflecting managers' past experience, the results indicate that in prediction models, export experience variables are more valuable than general experience variables. Born globals can be predicted with an accuracy of at least 90% in case of all applied machine learning methods, while the precision is lower in case of general fast internationalizers. The study leads to important implications for international business theory and practice.