Augment third-party risk management with a suite of digital solutions
By automating supplier onboarding workflow and gaining real-time visibility into their status in the process, you can unwind the complexities of managing disparate systems. This accelerates the due-diligence process and cuts costs.
Digital technologies can also screen millions of records of third-party data and news alerts to establish linkages and extract meaningful insights about possible risks.
Let's look at a few examples in which digital technologies, such as machine learning, artificial intelligence (AI), and automation, have enhanced third-party-risk management (TPRM):
Automated risk categorization
Building rules and risk-scoring algorithms into your system means you automatically categorize third parties based on their inherent risk exposure. These algorithms help identify the percentage of the supplier population that requires enhanced assessments.
Intelligent tagging and information deduplication
With intelligent tagging, you can identify and cluster similar datasets. For example, if multiple sources are reporting on a relevant news story, you still only see it once. This helps you cut through the noise to focus only on the most relevant red flags.
Digitized workflows and automated triggers
Introducing dynamic workflows to third-party risk assessment simplifies how you review and classify risk, making it easier to hone in on the insights from raw data. Automated triggers can then escalate the findings to relevant teams. For example, bribery issues go to compliance, labor violations go to the sustainability team, and so on.
Periodic screening with machine learning
Using machine learning, your organization can filter out false positives over a period of time with a fraction of the effort it would take to do so manually. With technology that learns to recognize the difference between a true hit and a false positive, you enhance the accuracy and efficiency of the alerts-clearing process (Figure 1).
A digital TPRM solution allows you to scan earlier assessments, identify patterns among true hits and false positives among the red flags, and automate the process. By understanding users' flagging and alert clearance behavior over time, you can highlight areas that need remediation.