Multidimensional Performance Assessment in Financial Contact Centers: An Interval-Valued Fuzzy Logic Approach to Debt Recovery Optimization

Businesses have seen increased difficulties in receiving payments due to the sluggish economic growth. This global tendency significantly impacts both affluent and impoverished nations. Enhancing debt collection practices is important to keeping economic stability. Debt collection firms and affiliated contact centers are enterprises that concentrate on securing payments for outstanding bills. Their proficiency simplifies the payment process. It is widely recognized that assessing the performance of a debt collection agency is challenging and often fraught with ambiguity. A performance measurement is a collection of statistically derived attributes that indicate the efficacy of an activity or its progress. Each project may be administered at an individual, team, departmental, or organizational level. The primary objective is to establish a systematic and unbiased methodology for debt collection by improving the performance of the call center. Initially, our research examined the issue of monitoring call center performance through an exploratory approach. The parent companies utilizing contact centers for debt collection and the call centers have convened numerous times. The proposed methodology offers an equitable assessment of performance criteria. This study initially presents indicator performance functions to ensure the outcomes are equitable. We formulate nine unique functions together with their corresponding parameters to objectively evaluate the effectiveness of the indicators, based on imprecise interval-valued Pythagorean fuzzy preference connections. This article is beneficial, as it presents a straightforward and adaptable method for monitoring the performance of a call center.

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