A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic Milica Latinovic 1 , Ivana Dragovic 2 , Vesna Bogojevic Arsic 3 , Bratislav Petrovic 4 1 Department of Financial Management and Accounting, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154 Belgrade, 11000, Serbia latinovicm@fon.bg.ac.rs 2 Department of Systems Theory and Control, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154 Belgrade, 11000, Serbia dragovic.ivana@fon.bg.ac.rs 3 Department of Financial Management and Accounting, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154 Belgrade, 11000, Serbia bogojevic@fon.bg.ac.rs 4 Department of Systems Theory and Control, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154 Belgrade, 11000, Serbia petrovic.bratislav@fon.bg.ac.rs Abstract This study proposes implementation of Boolean consistent fuzzy inference system for credit scoring purposes. Fuzzy inference system (FIS) allows domain experts to express their knowledge in the form of fuzzy rules, which enables combination of automatic rating with human judgment. Crucial for this model is that fuzzy rules are being evaluated using Boolean consistent fuzzy logic, which preserves all Boolean axioms. Experimental results show that the Boolean consistent FIS outperforms the conventional FIS in terms of classification accuracy, precision, and recall. Consistent fuzzy logic could contribute to the rightful approval of more loans which in turn would have positive effects on economic growth. Keywords: Fuzzy Inference System; Boolean Consistent Fuzzy Logic; Banks; Credit Scoring; Performance. 1. Introduction Micro, small, and medium enterprises (MSME) in developed and emerging economies represent an important sector, which needs to be vigorous in order to be able to generate economic development. This sector is essential for creating employment, increasing international trade, and establishing an entrepreneurship spirit. However, the banking sector is hesitant to lend to International Journal of Computational Intelligence Systems, Vol. 11 (2018) 414–427 ___________________________________________________________________________________________________________ 414 Received 19 September 2017 Accepted 1 December 2017 Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).