A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG DATA ANALYTICS

Registration ID: IJNRD_185444

Published ID: IJNRD2301007

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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.

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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

Research Area: Engineering

Author Type: Indian Author

Country: VISAKHAPATNAM, Andhra Pradesh, India

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

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

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