A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG DATA ANALYTICS

Registration ID: IJNRD_185444

Published ID: IJNRD2301007

DOI: Click Here to Get

WUPADRASHTA SAI KAUSHIK , CHALLAGUNDLA.HARSHITH , DURGA GOVARDHAN , URITI MADHU

Fraud Detection, Big data analytics, Apache Spark

The financial sector is currently undergoing digital transformation across products, services, and business models. This digitization aims to automate most of the manual financial transactions and other related services. Therefore, detecting fraud in financial transactions has become an important priority for all financial institutions. With modern technology and global communication, fraud has greatly increased and caused great damage. The focus of this paper is to test different approaches to detect fraud on a real data set of financial payment transactions. The dataset is obtained from Kaggle and consists of 6 million event records and 10 features with an event label of "fraudulent" or "non-fraudulent". These functions are investigated through exploratory data analysis and only 6 are kept for testing, such as payment type, account balance, transaction amount, etc. Two supervised machine learning algorithms, a random forest, and a support vector classifier are used to detect fraudsters transactions. The dataset is large and requires high computing power to process and train machine learning algorithms. Additionally, another challenge is the very uneven distribution between the fraudulent (0.1%) and non-fraudulent (99.9%) classes. This study aims to address both of these issues. To address the class imbalance, oversampling of minority class data using the Synthetic Minority Oversampling Technique (SMOTE) and under sampling of the majority class using random sub-sampling are investigated. Computational efficiency is achieved by implementing Apache Spark, which provides distributed processing for large volumes of data. The best performance is achieved using the random forest algorithm on the oversampled dataset with a precision of 99.95, an F1 score of 0.999, a recall value of 0.999, a geometric mean of 99.9%, and a model training time of 13.9 minutes. This article provides valuable insights into using large-scale, highly imbalanced big data sets to predict and generate financial fraud alerts.

Published Paper   E-Certificate

WUPADRASHTA SAI KAUSHIK, CHALLAGUNDLA.HARSHITH, DURGA GOVARDHAN, & URITI MADHU (January-2023). A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG DATA ANALYTICS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(1), a54-a58. https://ijnrd.org/papers/IJNRD2301007.pdf

Volume 8 Issue 1, January-2023

Pages : a54-a58

Paper Reg. ID: IJNRD_185444

Published Paper Id: IJNRD2301007

Downloads: 000122008

Research Area: Engineering

Country: VISAKHAPATNAM, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2301007.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2301007

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

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

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details