Optimized Hybrid Machine Learning Framework for Enhanced Financial Fraud Detection Using E-Commerce Big Data

Abstract

The threat of financial fraud is growing in the modern digital economy, especially
on e-commerce platforms. The sophisticated and quickly changing nature of
fraudulent activity is frequently too much for traditional fraud detection
techniques to handle. The optimized hybrid machine learning framework
presented in this study is intended to improve financial fraud detection through
the use of huge data from e-commerce. Through the incorporation of various
machine learning methodologies, including neural networks, decision trees, and
support vector machines (SVMs), the framework optimizes the advantages of each
methodology to yield exceptional detection accuracy and dependability. Largescale
e-commerce transaction data is first collected and preprocessed, and then
pertinent transactional and behavioral characteristics are extracted using the
suggested methodology. After that, the hybrid model is applied to these features
to find unusual transactions that might be signs of fraud. Through constant
monitoring and hyperparameter adjustment, the system is further improved to
accommodate novel fraud behaviors. This all-encompassing method seeks to
identify fraudulent activity effectively while quantifying the risks involved to
facilitate preventative actions that be taken in advance. Comprehensive trials
show that the framework works well, with notable gains in detection accuracy and
a decrease in false positives when compared to other approaches. In addition to
strengthening the financial security of e-commerce platforms, the study highlights
the potential of hybrid machine learning models to aid in the creation of more
durable and trustworthy fraud detection systems.