This paper discusses the limitations of traditional #asset#liability#management (#alm) techniques in #riskmanagement, particularly in high-interest rate environments, and proposes the application of #deep#reinforcement#learning (#drl) to overcome these limitations. The paper defines the components of #reinforcementlearning (#rl) that can be optimized for ALM, including the RL Agent, Environment, Actions, States, and Reward Functions. The study shows that implementing DRL provides a superior approach compared to traditional ALM, as it allows for increased #automation, flexibility, and multi-objective #optimization in ALM.
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