Customer Experience Enhancement in Omnichannel Banking Using Reinforcement Learning
Keywords:
Deep Reinforcement Learning, Omnichannel Banking, Customer Experience, Policy Gradient Methods, Personalization, Net Promoter Score, Reward FunctionAbstract
Mobile, internet, and branch banking consumers benefit from DRL. Deep Q-Networks (DQN) and Policy Gradient Methods see customer-bank interactions as dynamic agent-environment systems where reward-driven feedback improves engagement. Real-time decision-making, contextual data, and behavioral analytics boost consumer satisfaction. Simulations of multi-channel banking can quantify NPS, retention, and churn. Reward function design and exploration–exploitation trade-offs study system convergence and adaptive customization. DRL-driven digital banking systems may provide continuous learning environments for client and regulatory changes.
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