Insight

Third-party risk management: The road to AI

What combinations of AI tools for TPRM, organizational capital, and risk strategy might best bridge the gap between vision and reality?

Tarun Sondhi

Tarun Sondhi

Principal, Advisory, KPMG US

Greg Matthews

Greg Matthews

Partner, Advisory, FS Regulatory & Compliance Risk, KPMG US

+1 212-954-7784

Jonathan Dambrot

Jonathan Dambrot

Principal, Advisory, Cyber Security Services, KPMG US

+1 908-361-6438

Jilane Khakhar

Jilane Khakhar

Director, Advisory-Forensic, KPMG US

+1 212-954-1181

Anu Sandhu

Anu Sandhu

Specialist Director, Solution Management, KPMG US

+1 646-344-3052

Maribeth Timony

Maribeth Timony

Director, Solution Relations, KPMG US

+1 781-771-1765

In a networked global economy, organizations remain competitive by extending themselves through increasingly complex partnerships, alliances, and supply chains. This reliance on third parties, and more significantly, sharing confidential and sensitive information with entities that may be at much earlier stages of organizational maturity and risk management discipline, can lead to challenges around relationship-lifecycle risk monitoring. Third-party risk management (TPRM) owners voice concern that infrastructure constraints limit them to moment-in-time risk snapshots, and not the kind of continuous monitoring or re-verification that effective TPRM requires in rapidly changing conditions that we are seeing evolve at an unprecedented pace today.

 

The business case for artificial intelligence (AI)

The business case for AI in TPRM is both quantitative and qualitative. Enormous volumes of structured and unstructured data are required to stay ahead of third-party risk and can overwhelm even well-designed TPRM systems. AI algorithms, coupled with massive computing power, help organizations access data at scale and analyze the results in real time. Qualitatively, AI models can also elevate analytics maturity by enriching baseline data mining with predictive insight, and integration with decision-support prompts and alerts features that strengthen an anticipatory TPRM stance.

The promise of AI has moved well past theory and into the realm of practical considerations. While many have traditionally been skeptical of AI hype, CROs, CPOs, and boards are increasingly interested in exploring how AI can assist them. The question isn’t “whether” to incorporate AI into an arsenal of TPRM tools, but more typically “how, specifically?” or “where first?” or “in what order?”

Could AI assist third-party risk management for your organization? Read this paper to explore:

  • 4 big operational considerations
  • 7 common concerns for integrating AI functionality into TPRM models
  • 8 questions to ask external TPRM AI providers.
The application of AI to TPRM is never just a pure technology play. It can only improve and sustain programs that have solid processes and qualified people behind them.
Jilane Khakhar, Director, KPMG Forensic Services
Third party risk management: The road to AI
What combinations of AI tools for TPRM, organizational capital, and risk strategy might best bridge the gap between vision and reality?