Fraud Detection in Digital Payment Technologies Using Machine Learning

Junliang Wang

doi:10.5281/zenodo.10926495

Authors

Junliang Wang Johns Hopkins University

DOI:

https://doi.org/10.5281/zenodo.10926495

References:

22

Keywords:

Financial fraud, Machine learning, Dynamic integration selection, Double error measure

Abstract

Data has become the banking industry's most valuable asset, not only helping banks attract more customers, increase the loyalty of existing customers, make more effective data-driven decisions, but also enhancing business capabilities, increasing operational efficiency, improving existing services, enhancing security, and generating more revenue through all of these actions, and more. This paper explores the application of machine learning and artificial intelligence techniques in fraud detection in the financial sector. By analyzing the characteristics and common scenarios of financial fraud, the accuracy and efficiency of fraud detection model are improved by using dynamic integrated selection and double error measurement. The experimental results show that the machine learning model performs well in financial fraud detection, providing financial institutions with a more reliable and secure transaction environment, protecting the interests of customers and the stability of the financial market.

Author Biography

Junliang Wang, Johns Hopkins University

International Economics & International Political Economy, Johns Hopkins University, DC, USA.

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