Leveraging RFM Analysis and MOORA for Strategic Credit Decisions

Abstract

Credit risk management is crucial for the sustainability of businesses, particularly in industries dealing with perishable goods such as pharmaceutical distribution. This research aims to enhance credit risk assessment by integrating Recency, Frequency, Monetary (RFM) analysis with the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method. By analyzing six months of sales transaction data from a pharmaceutical distributor, an ETL process was conducted to consolidate data from customer, sales transaction, billing, payment, and product tables. Four key components—Recency, Frequency, Monetary, and Payment Timeliness—were normalized and weighted to calculate overall performance scores using MOORA. The research reveals that customers with higher Frequency and Monetary scores, recent purchase activity, and timely payments are identified as low-risk candidates. The top five customers were ranked based on their comprehensive scores, demonstrating the effectiveness of the combined RFM-MOORA approach. The results indicate that leveraging detailed customer behavior metrics can significantly improve credit risk management, reduce bad debts, and optimize credit allocation. This methodology supports data-driven, strategic decision-making, fostering stronger business relationships and financial stability. Future research should consider integrating advanced machine learning techniques and expanding data sources for even more precise risk predictions