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This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

The association between e-government and trust in government has been extensively examined, even though empirical works have returned conflicting conclusions about this association. This study aims to revisit the relationship between e-government and trust in government using macrolevel data from Organisation for Economic Co-operation and Development (OECD) member countries. It also addresses whether or not e-government should contribute to the quality of governance to improve trust in government.

Archival data from 36 OECD members over five years (2012–2015) were used to achieve the study objectives.

Results indicate that e-government does not directly affect trust in government, although there is an indirect association.

This implies that e-government systems will not significantly impact trust in government unless they contribute to the quality of governance that offers a basis for granting trust in government. Accordingly, value creation, including trust, should be considered during the design of e-government systems to achieve the desired outcomes.