Predicting bank failures has been an essential subject in literature due to the significance of the banks for the economic prosperity of a country. Acting as an intermediary player of the economy, banks channel funds between creditors and debtors. In that matter, banks are considered the backbone of the economies; hence, it is important to create early warning systems that identify insolvent banks from solvent ones. Thus, Insolvent banks can apply for assistance and avoid bankruptcy in financially turbulent times. In this paper, we will focus on two different machine learning disciplines: Boosting and Cost-Sensitive methods to predict bank failures. Boosting methods are widely used in the literature due to their better prediction capability. However, Cost-Sensitive Forest is relatively new to the literature and originally invented to solve imbalance problems in software defect detection. Our results show that comparing to the boosting methods, Cost-Sensitive Forest particularly classifies failed banks more accurately. Thus, we suggest using the Cost-Sensitive Forest when predicting bank failures with imbalanced datasets.

Digitalization is an inevitable fact due to increasing technology and the environment that challenges the status-quo of the traditional transactional systems. Especially during the Covid-19 pandemic, the adoption rate of eCommerce and digital transactions has increased significantly. In this research paper, we used eCommerce data of Hungary, Austria, Greece, and Sweden and their DESI index for 2017 to 2020 in order to understand the background of events which shows that the covid-19 pandemic had a significant impact on people’s perception of the day-by-day transactions and that the economic development of a country has a profound impact on the financial inclusion metrics and digitalization appetite of their citizens. Our main findings show that curfews during the pandemic significantly influence the volume of eCommerce transactions and DESI Index rankings are substantially linked with real-life matters.

Forecasting bank failures has been an essential study in the literature due to their significant impact on the economic prosperity of a country. Acting as an intermediary player, banks channel funds from those with surplus capital to those who require capital to carry out their economic activities. Therefore, it is essential to generate early warning systems that could warn banks and stakeholders in case of financial turbulence. In this paper, three machine learning models named as GLMBoost, XGBoost, and SMO were used to forecast bank failures. We used commercial bank failure data of Turkey between 1997 and 2001, where we have 17 failed and 20 healthy banks. Our results show that the Sequential Minimal Optimization and GLMBoost provide the same performance when classifying failed banks, while GLMBoost performs better in AUC and SMO when considering total classification success. Lastly, XGBoost, one of the most recent and robust classification models, surprisingly underperformed in all three metrics we used in research.