Dashboards help enable users to view all third parties and their risk levels. Another benefit of the pre-screening tool is Teva now has a vetted database of vendors and customers. This data- and analytics-driven approach has helped Teva shorten the time required to onboard third parties, drive consistency everywhere it does business, and reduce the cost of third-party due diligence.
Teva’s compliance leaders expect their data- and analytics-driven approach to reducing risk will continually become more proactive, while providing valuable insights that help in business planning and decision-making. Teva is exploring the use of advanced AI technologies to enhance pre-screening of third parties. Machine-learning algorithms can be trained on reviews, investigations, approvals and denials to create a predictive model that identifies risk indicators for potential new relationships.
While these advanced technologies are still in their early stages, they could eventually suggest new innovations for evaluating third parties. In addition, this system can easily be adapted for other compliance programs such as detecting counterfeit drugs or adhering to privacy regulations.
“We are leaning into compliance rather than looking in the rearview mirror,” Queisser says.
The primary goal of Teva’s compliance function is to provide business owners with the right tools, resources, policies and training to help them make better decisions. This new approach not only manages risk more efficiently — it also positions the compliance function as a driving force for change within the company and as a model for the entire industry.
This case study was first published by MIT Sloan Management Review Connections.