K-LSTM-ECM Model for Predicting Poverty Alleviation Impacts of Digital Financial Inclusion

Abstract

To evaluate the poverty alleviation effects of digital financial inclusion, this study proposes a comprehensive financial data analysis and prediction method by integrating K-means clustering, Long Short-Term Memory (LSTM) neural networks, and the Error Correction Model (ECM), collectively forming the K-LSTM-ECM model. The model first employs K-means clustering to group user data and uncover behavioral patterns of different user groups. Subsequently, LSTM is used to model and predict time-series data. Finally, the ECM is introduced to correct systematic errors and enhance prediction accuracy. The model was validated using diverse datasets, including World Bank Open Data, IMF economic indicators, and UNDP Human Development Reports. The results show that the error range of K-LSTM-ecm model is the lowest in mean square error, mean absolute error and root mean square error (e.g., mean square error is the lowest 1.44%), and the prediction precision rate reaches 91.23% on average. In terms of recall rate and false positive rate, K-LSTM-ecm model outperforms other models, with the highest recall rate reaching 94.45% and the lowest false positive rate reaching 2.08%. Through case studies, the prediction results of K-LSTM-ecm model for 2021 and 2022 are closer to the actual data, with poverty values of 0.212 and 0.181, respectively, and the prediction results of key indicators such as the proportion of subsistence population and rural disposable income are also better than other models. These findings verify the efficiency and reliability of the K-LSTM-ECM model in predicting the poverty alleviation effects of digital financial inclusion, providing robust data support for policymakers and the financial industry.