Published Paper Details:

With increasingly globalized supply chains and outsourced activities, third-party risk management (TPRM) has become an indispensable function for companies in all sectors. Conventional TPRM processes, commonly manual and passive, are incapable of coping with the rising numbers, complexity, and speed of third-party relationships. This article discusses the deployment of artificial intelligence (AI) to improve TPRM to facilitate proactive, scalable, and data-driven risk assessment and tracking. We introduce an end-to-end framework that employs machine learning models, natural language processing (NLP), and predictive analytics to drive vendor due diligence automation, detect emerging risks, and enable continuous monitoring. The model employs both structured and unstructured data sources like financial statements, regulatory filings, and online news sentiment to deliver dynamic risk scoring and real-time alerts. A financial services sector case study illustrates the efficacy of our method in the detection of high-risk suppliers and prevention of possible disruptions. We also touch on major challenges such as data quality, model explain ability, and ethics. Our results identify the potential of AI to revolutionize TPRM from a compliance function to a strategic asset that increases organizational resilience and governance.