{"references": ["C. Zopounidis, P. Pardalos and G. Baourak is, \"Fuzzy sets in\nmanagement, economics, and marketing\", 1st ed. River Edge, N.J.:\nWorld Scientific, 2001.", "L. Dymowa, \"Soft computing in economics and finance\", 1st ed. Berlin:\nSpringer, 2011.", "J. de Andres Sanchez, \"A Triangular Approximation for Fuzzy\nDiscounted Cash Flows Based on Financial Indicators\", Journal of\ncomputer and information technology, vol 1, iss 1, 2011.", "J. Buckley, E. Eslami and T. Feuring, \"Fuzzy Mathematics in\nEconomics and Engineering\", 1st ed. Heidelberg: Physica-Verlag HD,\n2002", "T. Korol, \"Fuzzy Logic in Financial Management\", Fuzzy Logic \u2013\nEmerging Technologies and Applications, 2012", "Z. Bro\\vz, \"Fuzzy logic as a tool for solving economical problems\",\n2009.", "M. Calder, \"A Framework For Conducting Risk Assessment\", Childcare\nIn Practice, vol 8, iss 1, pp. 7-18, 2002.", "K. Shang and Z. Hossen, \"Applying Fuzzy Logic to Risk Assessment\nand Decision-Making\", Casualty Actuarial Society, Canadian Institute of\nActuaries, Society of Actuaries, 2013.", "R. Ouache and A. A. J. Adham, \"Quantitative Risk Assessment in\nEngineering System using Fuzzy Bow-tie\", International Journal of\nCurrent Engineering and Technology, vol 4, iss 2, 2014.\n[10] H. Ishibuchi, T. Nakashima and M. Nii, \"Classification and modeling\nwith linguistic information granules\", 1st ed. New York: Springer, 2005.\n[11] M. Matsatsinis, K. Kosmidou, M. Doumpos and C. Zopounidis, \"A\nfuzzy decision aiding method for the assessment of corporate\nbankruptcy\", Fuzzy economic review, vol 8, iss 1, pp. 13--23, 2003.\n[12] E. Su and S. Li, \"A Financial Distress Prewarning Study by Fuzzy\nRegression Model of TSE-Listed Companies\", Asian Academy of\nManagement Journal of Accounting and Finance, vol 2, iss 2, 2006.\n[13] A. Lahsasna, \"Evaluation of Credit Risk Using Evolutionary-Fuzzy\nLogic Scheme\", Masters, Faculty of Computer Science and Information\nTechnology, University of Malaya, 2009.\n[14] J. Jang, C. Sun and E. Mizutani, \"Neuro-fuzzy and soft computing\", 1st\ned. Upper Saddle River, NJ: Prentice Hall, 1997\n[15] S. Naaz, A. Alam and R. Biswas, \"Effect of different defuzzification\nmethods in a fuzzy based load balancing application\", International\nJournal of Computer Science, vol 8, iss 5, 2011"]}

The assessment of the risk posed by a borrower to a lender is one of the common problems that financial institutions have to deal with. Consumers vying for a mortgage are generally compared to each other by the use of a number called the Credit Score, which is generated by applying a mathematical algorithm to information in the applicant’s credit report. The higher the credit score, the lower the risk posed by the candidate, and the better he is to be taken on by the lender. The objective of the present work is to use fuzzy logic and linguistic rules to create a model that generates Credit Scores.