Export Credit Insurance Markets and Demand

Krummaker, Stefan

doi:10.1002/9781119167402.ch20

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…For instance, public banks and agencies can help create synthetic loans to slot in funds alongside commercial lending at commercial interest rates and therefore expand existing lending capacity. The involvement of public banks and agencies demonstrates to commercial lenders and insurers through positive lending or underwriting decisions that financing projects in uncertain political and economic environments can be viable (Bischoff & Klasen, 2012; Kim & Yoo, 2019; Krummaker, 2020; Mah & Milner, 2005; Oramah, 2020).…”

Section: Climate Finance and Export Creditmentioning

confidence: 99%

“…International trade and foreign direct investment require sufficient, reliable, and affordable sources of financing. Imperfect information or information asymmetries between banks, project developers, or exporters and buyers prevent mutually beneficial investment from occurring (Heiland & Yalcin, 2020; Krummaker, 2020; Stiglitz & Weiss, 1981). EXIMs and ECAs step into the breach when commercial banks or private insurers do not offer sufficient facilities.…”

Section: Climate Finance and Export Creditmentioning

confidence: 99%

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Export finance and the green transition

et al. 2022

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CO 2 emissions are set to hit record levels in 2023, and there is no sign of peaking. The energy sector, in particular, is a key source of greenhouse gas emissions (GHG) and central to efforts to combat climate change. The sector contributed to approximately three-quarters of GHG in 2021 as most energy generation globally comes from fossil fuels. Despite increased climate ambitions and net-zero commitments, many governments still intend to raise oil and gas production, also a result of the economic consequences of the war in Ukraine. Only a moderate decrease in coal production is predicted over the next decade, although 195 countries committed to the Paris Agreement in 2015 (IEA, 2021;Kong & Gallagher, 2021;Olivier & Peters, 2020). The agreement responds to the threat of climate change by holding the increase in global average temperature to 'well below' 2°C in this century, as well as to pursue efforts to limit the rise to 1.5°C.Consequently, the global pathway to net-zero emissions by 2050 requires governments to implement and strengthen climate policies. This requirement led to a broad range of policy approaches, strategic directions, and concrete government actions in recent years. In November 2021, the 26th UN Climate Change Conference of the Parties in Glasgow (COP26) accelerated action towards the goals of the Paris Agreement. This included pathways to scale up renewable energy use in emerging markets and developing economies (EMDEs) as one of the most relevant mitigation measures to achieve the committed goals. On a national

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Section: Climate Finance and Export Creditmentioning

confidence: 99%

Section: Climate Finance and Export Creditmentioning

confidence: 99%

Export finance and the green transition

et al. 2022

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“…Often lenders are only willing to grant financing if export credit insurance is provided. Therefore, export credit insurance is regularly a key requirement for the realisation of an export transaction (Krummaker 2020).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Bärtl¹,

Krummaker²

2020

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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.

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Why banks insure structured commodity trade finance risk: evidence from a worldwide survey

Braun

Fischer

Schreiber-Orosz

2023

Geneva Pap Risk Insur Issues Pract

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We identify major drivers of the demand for credit insurance, using a worldwide survey among banking executives in the structured commodity trade finance business. Our results show that a bank’s propensity to purchase insurance increases in its experience and expertise with the product, the impact of insurance coverage on its balance sheet, the risk of the underlying transaction, as well as the intensity of broker relationships. Other factors, such as the size of the commodity trade finance portfolio, the competitiveness of the insurance price, and the risks arising from commodity price volatility, seem to be of lesser relevance.

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Export Credit Insurance Markets and Demand

Cited by 5 publications

References 33 publications

Export finance and the green transition

Export finance and the green transition

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Why banks insure structured commodity trade finance risk: evidence from a worldwide survey

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