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Predictive Analysis of Digital Payment Trends Using Deep Learning
Abstract
Digital transformation has significantly impacted the global financial landscape, particularly through the expansion of digital payment systems. This study investigates the trends and adoption patterns of digital payments from 2014 to 2024, leveraging advanced machine learning techniques for predictive analysis. Using an integrated deep learning model combining Bi-Stacked LSTM (Long Short-Term Memory) and Bi-Stacked GRU (Gated Recurrent Unit), we analyzed a comprehensive dataset derived from the Global Financial Inclusion Database and the Reserve Central Bank Consolidated Banking Database. The model effectively captures both long-term dependencies and short-term relationships in time-series data, providing valuable insights into the evolution of digital payment systems. Our approach offers an in-depth understanding of the factors influencing digital payment adoption, including demographic, regional, and policy-based variables, while also identifying challenges and growth opportunities in financial inclusion through digital platforms
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