A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG DATA ANALYTICS

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

"A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG DATA ANALYTICS", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 1, page no.a54-a58, January-2023, Available :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: 000121394

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

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

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

Indexing In Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar | AI-Powered Research Tool, Microsoft Academic, Academia.edu, arXiv.org, Research Gate, CiteSeerX, ResearcherID Thomson Reuters, Mendeley : reference manager, DocStoc, ISSUU, Scribd, and many more

How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-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: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area