Open Access

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.

Open markets, international trade and foreign direct investments are a source of prosperity in challenging times. This Special Section looks at developed economies and emerging markets, also taking into account the role of trade for impactful capacity-building in least developed countries (LDCs). Specific emphasis is placed on financing economic development and trade, analysing what roles trade and development finance should play in the quest for an efficient mobilisation of private capital for growth, trade and development.

Publicized as a global call for action in 2015, the United Nations General Assembly passed a resolution on the Sustainable Development Goals 2030 (SDGs). Before issuing the SDGs in 2015, the United Nations Conference on Trade and Development (UNCTAD) has already identified in 2014, as part of their World Investment Report, that especially developing countries are facing an estimated USD 2.5 trillion funding gap annually in the efforts to achieve the SDGs. Yet, the investment opportunities and challenges for investors, when contributing to the closure of this funding gap while benefiting from its economic potential have not been widely discussed. Despite that Foreign Direct Investments (FDI) are a key driver to sustainable economic growth and prosperity of a nation, policies and a holistic framework linking the 2030 Agenda to actionable investment opportunities for private investors are missing. Furthermore, a global platform capturing, channeling and promoting investment projects aiming to achieve the SDGs through impact investment has not been established. Utilizing global financial resources more effectively while developing new approaches and tools to promote impact investments, which demonstrate the benefits for investors to tap into the funding gap of the 2030 Agenda, will have the potential to significantly shape and influence the future of FDI.