Assessing the Effectiveness of Predictive Analytics in Fraud Detection: A Mixed-Methods Case Study

Description

Assessing the Effectiveness of Predictive Analytics in Fraud Detection: A Mixed-Methods Case Study

Abstract

The research assesses the effectiveness of predictive analytics in fraud detection. Fraudulent practices are a major problem in many major industries, especially in the financial industry. Predictive analytics aims to forecast future outcomes based on past and present data. However, using statistical and analytics models like predictive analytics offers valuable insights by identifying the data patterns. Besides, this report examines the efficiency of predictive analytics in recognizing fraud. Scammers utilize complex technologies in their activities which makes it difficult to identify the patterns to prevent fraud. Additionally, the report highlights the importance of predictive analytics and its role in preventing fraud.

The mixed-methods approach facilitates an understanding of predictive analytics and its importance in fraud detection. Such methodology examines the effectiveness and the accuracy of the predictive analytics models in fraud discovery. For instance, this is done by merging various research approaches. This research analysis will determine the reliability of the predictive analytics models by utilizing various performance metrics. Accordingly, such performance metrics involve the accuracy rate of detecting fraud, the time required for identifying fraud, and the false detection of illegal activities.

Moreover, the mixed technique involves detailed discussions on fraud identification using predictive analytics with cyber security experts, and data analysts. These discussions and surveys provide the actual data that helps in analyzing the effectiveness of predictive analytics. In addition, the report also explores the benefits and challenges in the application of predictive analytics models. However, the main intent of this report is to determine the effectiveness of predictive analytics and to provide valuable insights to security experts and fraud analysts. Finally, it assists them in devising better strategies to enhance the accuracy of the fraud detection analytic models.

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