Enhancing Fraud Detection

Computer Engineering

The telecommunications industry is grappling with the escalating problem of spam calls, which not only result in significant financial losses but also erode customer trust. Conventional spam call detection methods are often inadequate and resource-intensive, underscoring the need for more precise and efficient solutions. This study proposes a novel hybrid machine learning framework specifically designed to detect spam calls. By combining the strengths of supervised and unsupervised learning techniques, the proposed system uncovers hidden patterns and anomalies in call data, enabling accurate identification of spam calls. The framework incorporates a diverse set of features, including caller behavior analysis, call pattern examination, and audio signal characterization, to enhance the accuracy of spam call prediction. Experimental results based on a large dataset of labeled call records demonstrate that the proposed system achieves a precision of 92.5% and a recall of 90.2% in predicting spam calls, outperforming existing state-of-the-art methods. The findings of this research have significant implications for the development of effective fraud detection systems, enabling telecommunications service providers to proactively mitigate financial losses and enhance customer satisfaction. Abstracting fraud detection also encompasses the use of behavioral biometrics, which involves analyzing unique patterns in user behavior (e.g., typing rhythm, mouse movements, navigation patterns) to detect anomalies that may indicate fraudulent activity. Abstracting fraud detection involves developing sophisticated algorithms that can recognize patterns indicative of fraudulent behavior. This includes leveraging machine learning techniques such as anomaly detection, clustering, and pattern recognition to identify deviations from normal behavior. Instead of focusing on isolated data points, abstract fraud detection involves analyzing data across multiple dimensions. This includes transactional data, behavioral patterns, historical trends, and contextual information to build a comprehensive view of normal and abnormal activities.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.