A Type-2 Fuzzy Logic Based System for Decision Support to Minimize Financial Default in the Sudanese Banking Sector

Abstract

The recent global financial-economic crisis has led to the collapse of several companies from all over the world. This has created the need for powerful frameworks which can predict and reduce the potential risks in financial applications. Such frameworks help organizations to enhance their services quality and productivity as well as reducing the financial risk. The widely used techniques to build predictive models in the financial sector are based on statistical regression, which is deployed in many financial applications such as risk forecasting, customers’ loan default and fraud detection. However, in the last few years, the use of Artificial Intelligence (AI) techniques has increased in many financial institutions because they can provide powerful predictive models. However, the vast majority of the existing AI techniques employ black box models like Support Vector Machine (SVMs) and Neural Network (NNs) which are not able to give clear and transparent reasoning to explain the extracted decision. However, nowadays transparent reasoning models are highly needed for financial applications. This paper presents a type-2 fuzzy logic system for predicting default in financial systems. the researchers used a real dataset collected from the banking sector in Sudan. The proposed system resulted in transparent outputs which could be easily understood, analyzed and augmented by the human stakeholders. Besides, the proposed system resulted in an average recall of 83.5%, which outperformed its type-1 counterpart by 20.66%.