Deep Learning-Based Transfer Pricing Anomaly Detection and Risk Alert System for Pharmaceutical Companies: A Data Security-Oriented Approach

Abstract

This paper presents a new deep learning method for detecting cost variables in pharmaceutical companies, with a focus on information security and risk management. The proposed methodology combines deep learning with adaptive learning to address the challenges of data limitations and complex pricing structures in the pharmaceutical industry. The framework uses a hybrid neural network model combined with BiLSTM and monitoring techniques, achieving 94.7% detection accuracy while maintaining an error rate of less than 1.5%. The use of the transformational process resulted in a valuable knowledge transfer from a data-rich to a resource-enhanced scenario, leading to a 32% improvement in search results work for emerging markets. The security design includes military-grade encryption and access control functions, ensuring data privacy while facilitating cross-border compliance. The test results show a significant improvement over traditional methods, with a response time reduced from 48 hours to 2.3 hours and an estimated annual cost savings of $4.8 million for the business drugs in many countries.