EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.
How EY can help
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Harnessing the power of generative AI carries both risk and reward. EY teams are enabling clients to create holistic strategies and operating models.
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The EY survey offers four key takeaways:
1. Support from leadership matters. The organizations that scored the highest had buy-in from senior compliance leaders. Leadership support aligned with having a comprehensive vision and strategy and a level of investment that allowed for high-quality data and skilled team members. These served as key accelerators and outweighed other considerations, such as team size and technology stack, in driving success.
2. Analytics programs are evolving toward risk-based, data-driven compliance enablement. Organizations in the early stages of maturity are focused on automating repeatable functions to reduce manual efforts. More advanced programs employ regular monitoring dashboards and data-based risk assessments across a range of areas, including complaints, financial crimes, testing and monitoring, agent risk ranking, licensing and registration. As analytics programs mature, companies are developing more sophisticated use cases, such as leveraging AI and machine learning tools to assess agent behavior. There is also an increasing interest in incorporating analytics into nonfinancial risk management, including operational risk.
3. Proximity of the data and analytics team matters. Organizations reported having compliance analytics teams that ranged in size from three to 13 members and had a mix of experience in technical, compliance and business knowledge. Compliance functions with dedicated data analytics teams accelerated their programs more effectively than those that relied on a shared data service team. More successful compliance functions recognized the importance of cross-training to bridge the knowledge gap in analytics functions.
4. The most common roadblock is access to data. Disparate data sources are an ongoing challenge. Organizations are focusing on standardizing their data-sourcing methods to better operationalize analytics. Establishing data lakes can improve availability and lineage. Digital enablement of business processes through e-applications and electronic deliveries is streamlining the collection of customer data and policy information, reducing the barriers to data sourcing.
Conclusion
By investing in the power of AI and data analytics, risk and compliance leaders can transform compliance and broader risk management, making more efficient use of talent, increasing risk coverage and managing external regulatory pressures. A strategic, analytics-driven approach will allow companies to shift from a reactive mindset to a proactive one that creates value and enables an agile risk response.