  Corporate insolvency can have a devastating effect on the economy. With an
increasing number of companies making expansion overseas to capitalize on
foreign resources, a multinational corporate bankruptcy can disrupt the world's
financial ecosystem. Corporations do not fail instantaneously; objective
measures and rigorous analysis of qualitative (e.g. brand) and quantitative
(e.g. econometric factors) data can help identify a company's financial risk.
Gathering and storage of data about a corporation has become less difficult
with recent advancements in communication and information technologies. The
remaining challenge lies in mining relevant information about a company's
health hidden under the vast amounts of data, and using it to forecast
insolvency so that managers and stakeholders have time to react. In recent
years, machine learning has become a popular field in big data analytics
because of its success in learning complicated models. Methods such as support
vector machines, adaptive boosting, artificial neural networks, and Gaussian
processes can be used for recognizing patterns in the data (with a high degree
of accuracy) that may not be apparent to human analysts. This thesis studied
corporate bankruptcy of manufacturing companies in Korea and Poland using
experts' opinions and financial measures, respectively. Using publicly
available datasets, several machine learning methods were applied to learn the
relationship between the company's current state and its fate in the near
future. Results showed that predictions with accuracy greater than 95% were
achievable using any machine learning technique when informative features like
experts' assessment were used. However, when using purely financial factors to
predict whether or not a company will go bankrupt, the correlation is not as
strong.
