An Integrated G2M and RAFSI Model for Improved Leasing Customer Selection
Keywords:
Decision Support System, G2M Weighting, Leasing Customer Selection, RAFSI Method, Risk AssessmentAbstract
This research is motivated by the problem of high credit risk in leasing companies, which is caused by the process of selecting prospective customers that tends to be subjective, inconsistent, and reliant on a single indicator, making it less able to comprehensively reflect customer eligibility. This study aims to develop a model for selecting prospective leasing customers that is objective and systematic through the integration of the Grey Geometric Mean (G2M) method as a criteria weighting tool and the Ranking of Alternatives through Functional Mapping of Criterion Sub-Intervals into a Single Interval (RAFSI) method as a ranking technique. Assessment data were obtained from a number of customer candidates based on financial and non-financial criteria relevant to credit risk. The criteria weights were determined objectively using G2M, and then used in the evaluation and ranking process of alternatives with RAFSI based on the ideal and anti-ideal concepts. The results of the study showed that Customer Candidate 11 ranked first with a preference value of 0.74557, followed by Customer Candidate 5 in second place with a value of 0.65177, and Customer Candidate 14 in third place with a value of 0.62825. These findings demonstrate that the proposed model is capable of producing clear, consistent, and easily interpretable rankings, making it effective as a decision support system in the selection of leasing customer candidates and contributing to the reduction of credit risk.
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Copyright (c) 2026 Rakhmat Dedi Gunaawan, Junhai Wang (Author)
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