Enhanced Online Payment Fraud Detection Using Hybrid Machine Learning Mechanism

Abstract

"The rush in digital transactions has underscored the importance to develop accurate and trustworthy mechanism for detecting online payment fraud. In response to these challenges, author propose an innovative hybrid machine learning approach. The methodology integrates different algorithms to enhance the accuracy and resilience of fraud detection systems. The process initiates with the curation and preprocessing of a comprehensive dataset containing both genuine and fraudulent transaction data. Subsequent feature engineering extracts crucial insights from raw data, augmenting the model's discerning capacity. Random Forest classifier, Gradient Boosting classifier, AdaBoost classifier, and K-Nearest Neighbors classifier (KNN classifier) were judiciously chosen to develop an ensemble of model as an output of the research.

The main point of the methodology lies in the ensemble model creation phase, where predictions from individual models merge through voting mechanism leveraging the unique strengths of each classifier algorithm. This merging results in a more accurate and adaptable fraud detection system. Critical cross-validation and hyperparameter tuning are used to meticulously refine the ensemble model, ensuring optimal performance without overfitting.

The ensemble model demonstrated superior performance, significantly improving fraud detection rates while reducing false positives, thereby enhancing user experience. In summary, the proposed hybrid machine learning approach offers a strong and precise solution for online payment fraud detection. By using the strengths of various classifier algorithms, it strengthened accuracy and establishing a robust defence against the evolving landscape of digital payment fraud strategies.

Subject Descriptors: • Computing methodologies → Artificial intelligence → Machine learning → Hybrid learning approaches • Applied computing → E-commerce → Security and privacy in e-commerce → Fraud detection

Keywords: Machine learning, K-Nearest Neighbors classifier, Random Forest, Logistic Regression, Ensemble model, Voting, Cross-validation, Hyperparameter tuning"