International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Informative Article | Data & Knowledge Engineering | India | Volume 10 Issue 7, July 2021 | Rating: 5.2 / 10

Data Analytics and Anomaly Detection Techniques for Identifying Fraudulent Transactions in Oil & Gas Trading

Gaurav Kumar Sinha [8]

Abstract: The oil and gas trading sector represent a market of significant value that often finds itself exposed to the risk of fraudulent activities, which can lead to substantial economic damages. The pivotal role of identifying and averting fraud is indispensable for preserving the sector's integrity and financial success. This document delves into how data analytics and anomaly detection methods can be instrumental in pinpointing fraudulent transactions within the domain of oil and gas trading. An extensive collection of past trading data is utilized in this analysis, encompassing variables like the volume of transactions, pricing, information around trading partners, and prevailing market scenarios. The study applies a variety of data preprocessing steps which includes cleansing of the data, normalization processes, and the engineering of features to refine the data?s quality and pertinence. Exploring a myriad of anomaly detection algorithms forms the crux of this paper, spanning from statistical techniques, machine learning strategies, to deep learning models. Techniques of unsupervised learning such as clustering and analysis through principal components are leveraged for spotting abnormal patterns and anomalies in the trading figures. Furthermore, supervised learning models like decision trees, random forests, and machines of the support vector are put to use for differentiating between fraudulent and legitimate transactions, utilizing datasets with labels for training. Evaluating the anomaly detection methodologies hinges on standard metrics including the rates of accuracy, precision, recall, and the F1- score. The outcomes underscore the prowess of the introduced techniques in effectively identifying fraudulent transactions, where the models with top performance showcased high rates in detection and low incidences of false positives. This paper further contemplates the operational repercussions of integrating these anomaly detection models into the practical systems of oil and gas trading. Considerations around embedding these models into the current frameworks of risk management and the prospects for monitoring and alerts in real-time are thoroughly explored. To wrap up, the analysis illuminates the instrumental role of data analytics and the detection of anomalies in thwarting fraud within the oil and gas trading industry. These insights hold immense value for professionals in the industry, managers handling risks, and scholars, underscoring the critical need to adopt sophisticated analytical tools for securing the operations of trading.

Keywords: oil and gas trading, fraud detection, data analytics, anomaly detection, machine learning, unsupervised learning, supervised learning

Edition: Volume 10 Issue 7, July 2021,

Pages: 1529 - 1540

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