An evolutionary approach to preference disaggregation in a MURAME-based creditworthiness problem

• First application of the particle swarm optimization (PSO) to preference disaggregation problems in a MUlticriteria Ranking MEthod (MURAME) framework.
• Reformulation of the involved constrained optimization problem in terms of penalized unconstrained optimization problem for the PSO implementation.
• Application to large real credit scoring and credit ranking problems.

In this paper, we propose to use an evolutionary methodology in order to determine the values of the parameters for implementing the MUlticriteria RAnking MEthod (MURAME). The proposed approach has been designed for dealing with a creditworthiness evaluation problem faced by an important north-eastern Italian bank needing to score and/or to rank firms (which act as alternatives) applying for a loan. The point of the matter, known as preference disaggregation, consists in finding the MURAME parameters which minimize the inconsistency between the MURAME evaluations of given alternatives and those properly revealed by the decision maker (DM). To find a numerical solution of the involved mathematical programming problem, we adopt an evolutionary algorithm based on the particle swarm optimization (PSO), which is an iterative metaheuristics grounded on swarm intelligence. The obtained results show a high consistency between the MURAME outputs produced by the PSO-based solution algorithm and the actual scoring/ranking of the applicants provided by the bank (which acts as the DM).