Pemodelan Risiko Kredit Macet Kartu Kredit Menggunakan Agent-Based Modeling Studi Kasus pada Perilaku Konsumen

Abstrak

This research aims to analyze and predict credit card default risk using an Agent-Based Modeling approach. The model simulates consumer behavior in credit card usage, considering factors like income level, credit limit, and spending habits. The study utilizes secondary data and simulation results calibrated with empirical data. Simulations reveal that increased credit limits and consumer spending significantly contribute to rising default rates, while lower interest rate policies can mitigate default risk. These findings offer insights for financial institutions to design risk mitigation strategies and inform regulators in establishing more adaptive policies to consumer behavior dynamics. The study also recommends developing hybrid models incorporating machine learning approaches to enhance the accuracy of future credit card default risk predictions.