Enhancing Fintech Solutions with AI: Predicting Income and Financial Transactions Using Ensemble Learning Model

摘要

Financial technology (Fintech) has revolution-ized the financial sector by introducing innovative tools such as machine learning algorithms for predictive analytics, blockchain technology for secure and transparent trans-actions, and robotic process automation for streamlining and enhancing operational efficiency. These technological advancements have transformed conventional financial services into more accurate, reliable, and data-driven systems. One of the critical areas impacted by Fintech innovations is income prediction and the detailed analysis of transaction behaviors. By utilizing advanced machine learning methods, it is possible to evaluate customer financial health, identify risks, and provide personalized financial solutions. This study proposes a robust, reliable AI-based solution for income-level prediction and financial transaction analysis using ensemble learning models. The financial dataset employed for this study includes customer demographics, account activity records, and loan details. The data is meticulously preprocessed to ensure consistency, accuracy, and compatibility with the machine-learning models. Three state-of-the-art models, CatBoost, LightGBM, and Stacking Ensemble, have been implemented and evaluated for their predictive performance. Among the models tested, the Stacking Ensemble model demonstrates superior performance, achieving the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> score of 0.770 and the lowest mean squared error (MSE) of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.82\times 10^{7}$</tex>. CatBoost and LightGBM models also show strong results, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> scores of 0.734 and 0.689, respectively. This work underscores the importance of ensemble learning in fintech applications, such as credit risk evaluation, customer segmentation, and personalized financial services, paving the way for smarter and more efficient financial decision-making processes.