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Article December 9, 2025

AI Push Transforms Third Party Risk Workflows at Speed

AI Push Transforms Third Party Risk Workflows at Speed

As AI reshapes third party risk management, automation is accelerating contract reviews, questionnaire analysis and risk scoring. Yet rising efficiency brings new challenges around data quality, governance and oversight. Writing from the perspective of Ol

With sixteen years of experience building and managing third party risk programs across global financial institutions, Olga Baldwin has witnessed many evolutions in the field. Yet none, she says, match the pace and magnitude of the changes driven by artificial intelligence. 

Leading a global third party risk management team at StoneX Group Inc, she has seen firsthand how AI is reshaping vendor oversight, contract management and risk assessment across four regions of the world.

According to Baldwin, the most immediate change introduced by AI is speed. Tasks that once absorbed days or weeks of analyst time are now completed in minutes. Long questionnaires - sometimes exceeding a thousand questions - can be analyzed or even pre-populated automatically using existing data sets. 

AI tools can extract key terms from dense contracts, identify renewal dates and highlight termination clauses in seconds. For institutions managing sprawling supplier ecosystems, the operational shift is substantial.

AI-powered contract analysis has proven especially transformative. Natural language processing models can parse complex agreements, surface critical clauses and flag potential red flags that might be missed during manual review. 

Baldwin notes that this consistency reduces the risk of human error, particularly around deadlines or renewal cycles that carry financial consequences if overlooked.

In her experience, AI also improves risk prioritization, identifying elevated risks by rapidly integrating extracted data into inherent risk frameworks. 

This can reveal exposure that may previously have gone unnoticed. While the efficiency gains are undeniable, Baldwin stresses that they also come with challenges.

Chief among them is data quality. AI is only as accurate as the information it is trained on, and Baldwin has seen tools produce contradictory or inexplicable outputs when the underlying training data is incomplete or inconsistent. 

These situations, she says, reinforce why human oversight remains essential. AI can accelerate work, but it cannot yet replace expert judgment.

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