Article
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Machine Learning Insights Into Digital Payment Behaviors and Fraud Prediction
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Version 1
: Received: 16 July 2024 / Approved: 17 July 2024 / Online: 17 July 2024 (11:55:06 CEST)
How to cite: Zhang, X. Machine Learning Insights Into Digital Payment Behaviors and Fraud Prediction. Preprints 2024, 2024071431. https://doi.org/10.20944/preprints202407.1431.v1 Zhang, X. Machine Learning Insights Into Digital Payment Behaviors and Fraud Prediction. Preprints 2024, 2024071431. https://doi.org/10.20944/preprints202407.1431.v1
Abstract
With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.
Keywords
Digital payments; machine learning models; forecasting; financial services
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: 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.
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