Decision Support System for Selecting Peer-to-Peer Lending Applications using ARAS and Rank Sum Approaches

 (*)Elviza Diana Mail (Universitas Prof. Dr. Hazairin, SH, Bengkulu, Indonesia)

(*) Corresponding Author

Submitted: December 17, 2023; Published: January 31, 2024

Abstract

The development of information technology has brought major changes to the world of finance, especially with the emergence of new business models such as Peer-to-Peer (P2P) lending. Even though the potential for high profits can be obtained through P2P lending platforms, the challenges faced by investors in choosing the right investment application are increasingly complex. With so many choices in P2P lending applications, decision-makers must be careful in making their choice. This creates problems if the decisions made are incorrect, resulting in financial losses. So, the aim of this research is to build a decision support system for choosing a Peer-to-Peer lending application by applying a combination of the Additive Ratio Assessment (ARAS) method and Rank Sum weighting to make it easier for users to determine their choice. Based on the case study, the utility value obtained is highest to lowest, namely: Amartha Microfinance (A3) got a score of 0.9034, Asetku (A4) got a score of 0.8954, KoinWorks (A5) got a score of 0.8640, Investree (A2) got a score of 0.8484, and Danamas Lender (A1) got a score of 0.8080. Besides that, the usability test got an average score of 88.75%, which means the system is appropriate for its use and function. The resulting decision support system has features that make it easier for decision makers to determine P2P lending applications because the system developed can display alternative rankings.

Keywords


Additive Ratio Assessment; ARAS; Decision Support Systems; Peer-to-Peer Lending Applications; Rank Sum

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