Advanced Online Transactions Fraud Detection Using Machine Learning

Registration ID: IJNRD_215120

Published ID: IJNRD2403155

DOI: Click Here to Get

Abhishek K N , Akshay Kumar , Fani Kumar , Abhishek K , Namitha K Y

The frequency of fraudulent operations presents a serious peril to the security and integrity of digital fiscal systems, given the explosive increase of online deals. This study presents a new strategy to reduce online sale fraud by putting in place a strong fraud discovery system that makes use of machine literacy methods.Using slice- edge machine literacy ways, the suggested system models and analyzes sale data to make real- time distinctions between fraudulent and authentic deals. By rooting material information from a variety of sale attributes, point engineering and selection approaches are used to ameliorate the system's capacity to identify aberrantbehavior.In order to duly train the model, a large dataset with a variety of sale situations is named, guaranteeing that the system can acclimate to changing fraud trends. To determine which supervised literacy system is stylish for accurate fraud discovery, a variety of models are delved and varied, including decision trees, support vector machines, and neural networks.The system uses unsupervised literacy styles in addition to supervised literacy to identify new fraud patterns in the absence of labeled training data. The system can acclimate to new and unlooked-for fraud cases thanks to clustering algorithms and anomaly discovery techniques.Extensive tests are carried out using real- world sale datasets to validate the utility of the proposed system, and performance measures including perfection, recall, and F1 score are used to estimate the delicacy and responsibility of the system. The issues show how well the system can identify fraudulent deals while reducing false cons, which improves overall sale security.The study's findings offer perceptive information about the use of machine literacy in the field of detecting online sale fraud, giving fiscal institutions and other businesses a useful tool to secure their digital deals and safeguard the interests of stakeholders and guests. This study adds to the continuing sweats to develop robust and flexible results that can offset online fraud's dynamic character in the fleetly changing digital geography.

Published Paper   E-Certificate

Abhishek K N, Akshay Kumar, Fani Kumar, Abhishek K, & Namitha K Y (March-2024). Advanced Online Transactions Fraud Detection Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), b511-b513. https://ijnrd.org/papers/IJNRD2403155.pdf

Volume 9 Issue 3, March-2024

Pages : b511-b513

Paper Reg. ID: IJNRD_215120

Published Paper Id: IJNRD2403155

Downloads: 000121996

Research Area: Engineering

Country: Bangalore Urban, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403155.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403155

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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