How AI is Revolutionizing Third-Party Risk Management and Compliance

How AI is Revolutionizing Third-Party Risk Management and Compliance

Third-party relationships represent a critical compliance risk, exposing organizations to potential bribery, corruption, reputational harm, and regulatory violations. Historically, companies addressed these risks with periodic due diligence and reactive measures. However, according to an article by Tom Fox on the Compliance Podcast Network, the DOJ’s 2024 Evaluation of Corporate Compliance Programs (ECCP) emphasizes continuous monitoring and data-driven approaches, making traditional third-party risk management methods inadequate.

The article explains how AI transforms third-party risk management by enabling real-time screening, onboarding due diligence, continuous monitoring, and periodic risk reassessments.

Initial Screening: AI aggregates diverse data sources, including public records, social media, and litigation databases, to identify hidden risks. It employs natural language processing to flag ethical or regulatory concerns and assigns scored risk profiles for prioritization.

Onboarding Due Diligence: AI streamlines document review, identifies beneficial ownership through cross-referenced data, and uses predictive analytics to assess future misconduct risks. These capabilities align with DOJ guidance on transparency in third-party relationships.

Continuous Monitoring: AI-driven systems provide 24/7 alerts on legal, reputational, or regulatory developments. Advanced analytics detect transaction anomalies and behavioral shifts, addressing emerging risks by proactively addressing emerging risks.

Risk Re-Evaluation: AI adapts to evolving business environments, analyzing geopolitical changes, industry trends, and regulatory updates. Customizable models ensure ongoing alignment with DOJ expectations.

Despite its benefits, the article notes that AI implementation requires robust data governance, algorithm transparency, and ethical deployment to comply with privacy laws and prevent inefficiencies. Addressing these challenges ensures AI remains effective and accountable.