Utilizing Pega Decisioning for Data-Driven Dispute ResolutionStrategies

Abstract

In the complex landscape of financial services, efficient dispute resolution is paramount for maintaining customer trust and operational excellence. This study explores the application of Pega Decisioning to develop data-driven strategies for dispute resolution. Traditional methods, often characterized by manual processing and systemic inefficiencies, struggle to meet the demands of modern financial institutions. By leveraging Pega Decisioning’s advanced capabilities in predictive and adaptive analytics, ABC Bank was able to automate and optimize its dispute resolution processes. The implementation led to a substantial 66% reduction in average resolution time, a 30% decrease in operational costs, and a 20% improvement in resolution accuracy. Customer satisfaction scores also saw a significant uplift, underscoring the benefits of quicker and more precise dispute handling. This paper provides a comprehensive analysis of the deployment process, the challenges encountered, and the measurable outcomes, demonstrating the transformative impact of integrating AI and machine learning into business process management. The findings offer valuable insights for organizations seeking to enhance their dispute resolution frameworks through data-driven decisioning solutions.