MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations


Authors

  • Setiawansyah Universitas Teknokrat Indonesia, Bandarlampung, Indonesia
  • Sanriomi Sintaro Universitas Sam Ratulangi, Manado, Indonesia
  • Ahmad Ari Aldino Monash University, Victoria, Australia

DOI:

https://doi.org/10.47065/jieee.v3i2.1628

Keywords:

Customer; Loan Eligibility; MAUT; PIPRECIA; Recommendations

Abstract

Customer loan eligibility recommendations are an important step in the risk and financial assessment process. To ensure business continuity and customer satisfaction, it is important for financial institutions to consider several key factors. Problems in determining lending include various aspects that include credit risk that is difficult to assess appropriately, insufficient information about potential borrowers, potential fraud that can harm lenders. Regardless, financial institutions must ensure sound risk management and comply with applicable regulations and compliance standards to minimize potential losses and maintain fairness in decision-making related to lending. Cooperatives act as institutions that provide access to loans with more flexible terms than conventional financial institutions. The loan application process involves evaluating the customer's eligibility, which includes an analysis of financial condition, credit history, and repayment capacity. The contribution of this study provides a recommendation for savings and loan cooperatives in lending to customers using a decision support system model. The purpose of this study is to evaluate and compare customer loan feasibility using a combination of Multi-Criteria Decision Making (MCDM) methods, namely Multi-Attribute Utility Theory (MAUT) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA). This study aims to determine the ability of both methods to provide a comprehensive understanding of loan feasibility by considering a number of relevant criteria. The results of the customer loan eligibility rating recommend rank 1 with a final value of 0.5127 obtained by Customer K, rank 2 with a final value of 0.432 obtained by Customer I, and rank 3 with a final value of 0.3559 obtained by Customer F.

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Published: 2023-12-30
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