The South African banking industry has evolved from simply plugging technologies into its systems to a data-intensive domain. With data disrupting every known market, there is a significant need to take advantage of the magnitude and detail of data captured by the enterprise to augment...

Mobile checkout in the retail store has the promise to be a rich source of big data. It is also a means to increase the rate at which big data flows into an organization as well as the potential to integrate product recommendations and promotions in real time. However, despite efforts by...

The effects of the 2008 financial crisis undoubtedly caused problems not only to the banking sector but also to the real economy of the developed and the developing countries in almost all around the globe. Besides, as is widely known, every banking crisis entails the corresponding cost to the...

This paper evaluates the predictive performance of various factor estimation methods with big data. Extensive forecasting experiments are examined, using 7 factor estimation methods with 11 decision rules that determine the number of factors. First, the number of estimated factors used in...

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that...

The qualitative information of companies’ financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this research, a dataset of 924,903 financial statements from 355,704 German companies classified into solvent, financially distressed,...