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Open Access Journal Article

Predictive Modeling for Fraud Detection

by Daniel Thomas 1,*
1
Daniel Thomas
*
Author to whom correspondence should be addressed.
MAD  2022 4(1):30; https://doi.org/10.69610/j.mad.20220522
Received: 24 March 2022 / Accepted: 29 April 2021 / Published Online: 22 May 2022
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Abstract

The purpose of this paper is to investigate the effectiveness of predictive modeling in the field of fraud detection within financial institutions. Fraud detection is a crucial task for businesses to protect themselves against financial loss and maintain the integrity of their operations. Traditional methods of fraud detection often rely on manual review and rule-based systems, which are time-consuming and prone to errors. In contrast, predictive modeling offers a more efficient and accurate approach by analyzing historical data to identify patterns and anomalies that may indicate fraudulent activity. This study explores the application of various machine learning algorithms, such as logistic regression, decision trees, and neural networks, to predict fraud occurrences. The results demonstrate that predictive models can significantly enhance the detection rates of fraudulent transactions while reducing false positives. Additionally, this paper discusses the challenges associated with implementing predictive models in the real world, including data quality, model interpretability, and the potential for model bias. By addressing these challenges, the study aims to provide insights into the development and deployment of robust predictive models for fraud detection.

Keywords: Predictive Modeling; Fraud Detection; Machine Learning; Financial Institutions; Anomaly Detection.;
Copyright: © 2022 by Thomas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Thomas, D. (2022). Predictive Modeling for Fraud Detection. Management Analytics and Decision, 4(1), 30. doi:10.69610/j.mad.20220522
ACS Style
Thomas, D. Predictive Modeling for Fraud Detection. Management Analytics and Decision, 2022, 4, 30. doi:10.69610/j.mad.20220522
AMA Style
Thomas D. Predictive Modeling for Fraud Detection. Management Analytics and Decision; 2022, 4(1):30. doi:10.69610/j.mad.20220522
Chicago/Turabian Style
Thomas, Daniel 2022. "Predictive Modeling for Fraud Detection" Management Analytics and Decision 4, no.1:30. doi:10.69610/j.mad.20220522

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ACS Style
Thomas, D. Predictive Modeling for Fraud Detection. Management Analytics and Decision, 2022, 4, 30. doi:10.69610/j.mad.20220522
AMA Style
Thomas D. Predictive Modeling for Fraud Detection. Management Analytics and Decision; 2022, 4(1):30. doi:10.69610/j.mad.20220522
Chicago/Turabian Style
Thomas, Daniel 2022. "Predictive Modeling for Fraud Detection" Management Analytics and Decision 4, no.1:30. doi:10.69610/j.mad.20220522
APA style
Thomas, D. (2022). Predictive Modeling for Fraud Detection. Management Analytics and Decision, 4(1), 30. doi:10.69610/j.mad.20220522

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