Abstract. Credit scoring is the main process of credit transactions in assessing the credit risk of credit applicants. Unfortunately, in practice, its implementation only stops to the credit approvals. In this research, we utilize credit scores to generate the customized credit prices. We believe that each person has their own credit risk so that they will get different credit prices depend on their individual credit risk. This credit risk-based pricing is optimized by reinforcement learning approaches to represent the dynamic solution related to the updated credit historical data. There are several variables considered in the profit optimization model such as credit scores, tenor, credit prices (or rate for credit applicants), and plafond. We implement this solution to the random generated credit data.
Reinforcement Learning on the Credit Risk-Based Pricing
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