AI-Driven Predictive Modeling for Personalized Bill Payment Reminders in Digital Financial Platforms

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.

Introduction

The study explores the development and enhancement of AI-based prediction systems to forecast utility bill payment dates in India, a need accelerated by the surge in digital payment adoption during the COVID-19 pandemic. Digital platforms have become essential for everyday financial tasks, but predicting when a customer will next pay a utility bill remains a challenge due to the scale and complexity of consumer behavior and real-time financial data.

Key Points:

1. Motivation and Need:

• The pandemic increased the use of cashless transactions, especially for utility bills (electricity, water, telecom).

• Financial institutions aim to predict payment dates to improve customer engagement, send reminders, and optimize marketing.

• Accurate predictions help improve financial planning, reduce delays, and boost platform loyalty.

2. Technological Approach:

• Traditional models fall short in handling complex behavior.

• The paper proposes a system using advanced AI techniques, including:

  • Transformer-based Time Series Forecasting (TFT) for long-term pattern detection.

  • Graph Neural Networks (GNNs) for capturing customer relationships and behavior.

  • LSTM and GRU models to understand temporal sequences in payment history.

• Explainable AI (XAI) tools like SHAP and LIME are used to ensure transparency and regulatory compliance.

3. Contribution of the Paper:

• Proposes a novel AI system combining TFT, GNNs, LSTM, and GRU for predicting utility bill payment dates.

• Incorporates external factors like holidays and customer demographics.

• Demonstrates higher accuracy than traditional methods.

• Supports personalized customer communication (e.g., notifications, reminders).

4. Related Works:

• Previous research focused mainly on retail purchase prediction using ML models like ANN, XGB, Random Forest, and ARIMAX.

• These models, though insightful, lack utility-specific features and temporal context relevant for utility bills.

• The present study addresses this gap by integrating complex consumer behavior, utility types, and external variables.

5. Methodology:

• Data Collection: Includes over 215,000 records from Indian online platforms covering multiple utility types.

• Feature Engineering:

  • Transaction frequency (T_i), minimum and average days between payments.

  • Impact of holidays (H_i) on transaction patterns.

  • Encoding methods (One-Hot, Ordinal) for categorical data.

• Model Evaluation: Uses metrics like BER, OSNR, constellation diagrams, and RF spectrum to evaluate model performance.

Conclusion

Through this study, a great achievement has been made in the application of these advanced AI techniques, which are, for instance, Transformer-based Time Series Forecasting, Graph Neural Networks, and deep learning architectures like LSTM and GRU, to anticipate the next payment of the utility bill. Proposed models showed high predictive accuracy, with TFT outperforming other methods in terms of accuracy, precision, and error rates. The implementation of transparency AI methods that are included in SHAP and LIME made it possible to create models that are not only accurate but also interpretable, which means they meet regulatory requirements. The results show what AI can do if used in predicting customer engagement, marketing strategies, and financial planning for both the clients and the providers. This study is a part of the increasing number of AI-based research in the financial sector, providing new knowledge on the use of deep learning and XAI in time-related prediction tasks.

References

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Copyright

Copyright © 2025 Rahul Azmeera, Srikanth Soma, Tushar Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.