A2GAN: A Deep Reinforcement-Based Learning Algorithm for Risk-Aware in Finance

Abstract

Financial data suffer from missing, unlabeled and unbalanced data, thus weakening the performance of decision-making systems. In addition, the aim of financial institutions is not only to find decision-making models that achieve high scores for the standard metrics (e.g., AUC, accuracy, F-score) but to reduce the risk from miss-classification cases. This paper addresses these problems by proposing a novel framework inspired by reinforcement learning, specifically actor-critic, for decision-making and implementing generative adversarial networks for imputing missing data, as well as utilizing the unlabeled dataset. Moreover, by taking advantage of reinforcement learning, the trained model is calibrated using a customizable reward function, which can be designed for different purposes of financial institutions. We evaluate the framework via real-world financial datasets that only have a small amount of labeled data and exhibit missing data. Our experiment shows promising results where the financial risk is dramatically reduced without too much sacrifice on standard metrics.