Corresponding Author's : Seema Aggarwal

Co-Author's : geeta Aggarwal

Abstract :

The widespread implementation of digital payment methods has greatly simplified financial transactions but has also increased the potential for online payment fraud. Creating reliable prediction models for fraud detection is crucial for protecting against fraudulent actions. This research employs machine learning techniques, notably Random Forest or Logistic Regression, to differentiate between genuine and fraudulent financial dealings reliably. Researchers assess these models' efficacy in real-time fraud detection using a comprehensive dataset, including transaction information and labelled fraud incidents. The findings of this study will help strengthen the safety and trustworthiness of online payment systems, reducing the risk of fraud and other security breaches for consumers.

Keywords :

Cybercrime, Fraud, Digital Payment

Article Details

Unique Paper ID: 2110

Publication Volume & Issue: VOLUME 4 , ISSUE 2

Page(s): 47-53

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