Sovereign debt and currencies play an increasingly influential role in the development of
any country, given the need to obtain financing and establish international relations. A recurring
theme in the literature on financial crises has been the prediction of sovereign debt and currency crises
due to their extreme importance in international economic activity. Nevertheless, the limitations
of the existing models are related to accuracy and the literature calls for more investigation on the
subject and lacks geographic diversity in the samples used. This article presents new models for the
prediction of sovereign debt and currency crises, using various computational techniques, which
increase their precision. Also, these models present experiences with a wide global sample of the
main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe,
and globally. Our models demonstrate the superiority of computational techniques concerning
statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis:
fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees,
and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting,
random forests, and deep belief network. Our research has a large and potentially significant impact
on the macroeconomic policy adequacy of the countries against the risks arising from financial crises
and provides instruments that make it possible to improve the balance in the finance of the countries.