AI-Driven Predictive Modeling for Personalized Bill Payment Reminders in Digital Financial Platforms
Year: 2025
Type: article
Abstract: One of the positive results of the digital payment revolution in India over the years has been brought about by the need for contactless payment technology, and it has quickly turned from a luxury to a norm during the ongoing pandemic. In the digital transaction ecosystem, there is an improvement in the care done, where one can conveniently and quickly pay the bills, like utility bills such as electricity, water, telephone, car payment, mortgage, and so on. The accurate prediction about the next bill payment date is vital for banks with respect to marketing strategies, which are planned to be optimized, customer engagement to be fostered, and the delivery of targeted reminders to be done. However, the prediction of the transaction timelines still remains a major concern because of the unpredictable rise and the complexity of the real-time financial data. This paper describes the utilization of cutting-edge AI methods for the exact energy bill payment prediction. The new system uses Transformer-based Time Series Forecasting (TFT) to predict long-term dependencies; Graph Neural Networks (GNNs) are used for customer verifications as well as deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to enable temporal sequence analysis. Besides, Explainable AI (XAI) methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), are combined to make models more interpretable and compliant with the rules. Experimenting shows that the proposed method can provide better predictive capability, which allows personalized marketing campaigns and timely payment reminders. Blonde intelligence, AI-based solutions—through mobile notifications—bring benefits like user experience enhancement and personal financial and payment management, and they attract digital payment service providers.
Author: Rahul Azmeera
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408230320