ANALYZING CUSTOMER SUPPORT LOGS FOR ANOMALY DETECTION USING DEEP LEARNING

Abstract

This project investigates the application of deep learning techniques for anomaly detection in customer support logs, a critical resource for insurance companies. The increasing volume of customer interactions generates vast amounts of data, making it challenging to manually identify irregularities that may indicate fraud or data entry errors. By leveraging advanced data-driven methods, we developed a robust deep learning model capable of analysing historical support logs to detect anomalies with high accuracy. The study employs various neural network architectures, including convolutional and recurrent networks, to capture complex patterns within the data. We also explore feature engineering techniques to enhance model performance and ensure the reliability of the detection process. The results demonstrate significant improvements in identifying anomalies compared to traditional methods, underscoring the potential of deep learning in automating and streamlining customer support operations. Furthermore, the project emphasizes the importance of enhancing data quality, implementing real-time detection systems, and automating alerts to improve overall customer support efficiency. By addressing these challenges, our findings contribute to the development of more effective strategies for managing customer interactions and safeguarding against potential fraud, ultimately leading to better service delivery and customer satisfaction.