Business Process Automation in Pega BPM Using Generative Adversarial Networks (GANs)

Abstract

Modern corporate operations depend critically on business process automation (BPA), which helps companies to increase efficiency, save costs, and improve decision-making by means of enhanced performance of their operations. Although conventional rule-based automation systems may lack scalability and adaptability, Pega Business Process Management (BPM) is extensively applied for workflow automation. This work investigates the integration of Generative Adversarial Networks (GANs) with Pega BPM to develop an AI-driven, self-learning automation framework enhancing process optimisation, anomaly detection, and decision-making. Leveraged in this study to forecast process inefficiencies, optimise workflows, and improve decision support systems inside Pega BPM, GANs—known for their capacity to create synthetic yet realistic data—are We present a self-improving automation system that constantly refines business processes depending on real-time data by training the generator model to replicate optimal processes and the discriminator model to assess their efficacy. The work also looks at how Reinforcement Learning (RL) may complement GANs to provide context-aware, adaptive process automation. Moreover, this study tackles issues with AI-driven process automation including data privacy, security issues, and AI-generated process suggestion bias. To improve data security and regulatory compliance (GDPR, CCPA) while preserving high-performance automation capability, we suggest the merging of homomorphic encryption with federated learning. By means of empirical analysis and case studies, we assess the effects of GAN-powered BPA in Pega BPM on process efficiency, mistake avoidance, and decision correctness. Results show that, providing a scalable and intelligent solution for businesses, AI-driven automation greatly increases workflow optimization and company agility. This paper offers a disciplined methodology for using GANs in BPM to improve operational efficiency and future-proof corporate automation plans.