9 Emerging Use Cases for AI in TPRM

The integration of AI into third-party risk management (TPRM) is transforming the way organizations approach their relationships with external partners. AI, with its ability to process vast amounts of data and identify patterns, offers a new level of insight and efficiency that traditional methods simply cannot match. TPRM covers a wide range of compliance risk topics ranging from data privacy to anti-corruption to trade sanctions. This makes effective and efficient risk management even more challenging and the need for AI more compelling.

With that said, professionals responsible for TPRM are still working to understand how best to integrate AI within their processes to enhance, not replace, human oversight and intelligence. Based on discussions with leaders in the space and a review of technologies supporting them, the experts at Ethisphere and GAN Integrity recently released a report that compiles a list of nine emerging use cases for AI within the TPRM realm along with a framework for prioritizing which to implement first (alongside much more to support your organization build a stronger TPRM programs by automating critical functions).

9 use cases for AI in TPRM

1. Automated due diligence: AI-driven background checks can scan large volumes of public data sources for adverse media and watchlists to identify potential risks.
2. Contextual risk assessments: AI creating bespoke risk assessments and pre-populated questionnaires.
3. Continuous monitoring: Machine learning models can assign dynamic risk scores, alerting compliance when significant changes occur based on changing regulations or new situations on the ground.
4. Pattern detection: Identify anomalies in invoicing or contractual relationships that may signal fraud, corruption or the use of unauthorized sub-contractors.
5. Automated document review: AI scans contracts, financials and compliance reports for red flags and anomalies across the full range or relevant compliance risk topics.
6. AI-driven contract analysis: Machine learning to detect risky clauses, deviations from standard terms or hidden obligations.
7. Decision support: AI provides risk experts with machine-learning-based recommendations. (e.g. to approve or deny a third party.).
8. Automated triage & risk scoring: AI-based classification and prioritization of third parties, helping teams focus on the highest-risk entities first.
9. AI-driven ownership & relationship mapping: AI supported graph analytics to uncover complex ownership structures, beneficial owners (UBOs) and hidden relationships.

Prioritizing AI use cases

Understanding the various use cases for implementing AI within your TPRM approach is one thing, while knowing which to select and implement first is another — one that requires an understanding of both change management and the relationship between AI and user. This simple three-step approach will help prioritize which use case to begin with and how to plan for its implementation:

• Identify compliance pain points: Clearly define the specific regulatory, ethical or operational issues AI will address before deploying solutions.
• Solve practical problems: Focus AI efforts on enhancing compliance efficiency and accuracy, not adding unnecessary complexity. It is critical to start with a clearly defined problem and determine if the needed data is available before you see how AI can be applied.
• Enhance, don’t replace: AI should support human decision-making, not replace it. Effective AI complements expertise with enhanced data processing capabilities.

Want to learn more about how AI and automation technologies can support your program’s growth during challenging times? Read the full joint report by Ethisphere and GAN Integrity and understand the growing trend of incorporating AI into TPRM practices. The report highlights the expanding third-party risk environment that is leading to a greater need for AI-driven tools to automate and streamline risk assessment processes — alongside how your program can successfully do so.