A1387
Title: Reinforcement learning for credit risk
Authors: Jorge C-Rella - University of A Coruna (Spain) [presenting]
Juan Vilar Fernandez - Universidade da Coruna (Spain)
Ricardo Cao - Universidade da Coruna (Spain)
David Martinez Rego - DataSpartan (United Kingdom)
Abstract: The problem of credit risk is dynamic, as customers' payment behavior evolves according to the economic cycle and market trends, and cost-sensitive, as the results depend on the amount of the loan. In addition, information is only available for approved transactions, which can lead to unfair biases and opportunity costs. New dynamic learning strategies are proposed that extend online learning and bandit algorithms to cost-sensitive learning. Experiments on simulations and real-world datasets demonstrate the effectiveness of the proposed algorithms, opening the door to extending new methods to credit risk and other cost-sensitive problems.
