Fraud Detection: Anomaly Detection System for Financial Transactions

Registration ID: IJNRD_208762

Published ID: IJNRD2311260

DOI: Click Here to Get

Harshit , GARIMA , Aparna , Prabhjot Kaur , Neetu Bala

— Fraud Detection, Machine Learning, Data Mining, Anomaly Detection, Feature Engineering, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data, Financial Fraud, Credit Card Fraud, Fraud Prevention, Fraud Detection Algorithms

In the rapidly evolving landscape of digital financial transactions, the need for effective fraud detection systems is paramount. This paper presents Fraud Guard, an anomaly detection system designed to identify fraudulent financial transactions from a comprehensive dataset of historical transactions. The system employs a multi-faceted approach that includes data collection, preprocessing, and feature engineering to enhance its detection accuracy. By extracting relevant features from transaction data and considering external data sources such as IP geolocation, Fraud Guard aims to provide a robust solution to combat financial fraud.

Published Paper   E-Certificate

Harshit, GARIMA, Aparna , Prabhjot Kaur , & Neetu Bala (November-2023). Fraud Detection: Anomaly Detection System for Financial Transactions. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), c472-c477. https://ijnrd.org/papers/IJNRD2311260.pdf

Volume 8 Issue 11, November-2023

Pages : c472-c477

Paper Reg. ID: IJNRD_208762

Published Paper Id: IJNRD2311260

Downloads: 000121989

Research Area: Engineering

Country: Gharuan, PUNJAB, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311260.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311260

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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