Paper Title
Sovereign Default Risk Prediction using AI

The phenomenon of sovereign defaults on external debt repayments has been a problem staring financial institutions in the face. This problem has persisted for a long time despite the set of rules governing sovereign borrowing and repayment. It manifests negative effects in the international financial institutions such as World Bank which holds a significant number of sovereign bonds in its portfolio. A default occurrence, for example, weakens the financial institution’s balance sheet and these problems in general threaten the bank run. This research study develops a new method that uses artificial intelligence (AI) to predict the risk of credit default of a potential borrower country. This method ensures accurate and reliable assessment leading to informed decisions in the financial institution. Although there are sources that currently help determine the credit rating of a country such as credit rating agencies (CRAs) but AI methods have been recommended from recent studies as a more accurate method and has been shown to outperform CRAs in prediction. Therefore, a more accurate default risk prediction from this study will be helpful in assessing the risk of sovereign credit defaults, thus, strengthening the international financial institutions. Keywords - Artificial Intelligence, Credit Default Swaps, Loan, Neural Network, Prediction, Risk