Compliance and legal teams are struggling to keep pace with an ever-evolving regulatory and legal landscape.
Responsibilities range from constantly tracking employment and office safety standards to understanding complex rules governing ethical employee conduct to dealing with the monumental regulatory, legal, privacy and cost challenges created by new technologies such as the cloud, social media and the Internet of Things (IoT). Knowing the rules of the road can be every bit as difficult as ensuring employees follow them, which in turn can create critical gaps in both rule tracking and employee monitoring that lead to waste, fraud, abuse and other practices that put a company at risk.
The difficulty of this struggle is exemplified by the state of compliance with the European Union’s General Data Protection Regulation (GDPR). Despite more than two years of high-profile educational efforts from the EU, legal publications and solution vendors, a recent survey from Deloitte found that only 35 percent of respondents felt they could demonstrate a “defensible position” on GDPR compliance. Even more surprising, a DemandBase survey found that only 32 percent of respondents were fully GDPR compliant and 20 percent were completely unaware of the regulation.
As the implementation challenges around the new California Consumer Privacy Act suggest, complying with privacy regulations will only get more complicated. The particularly bad news for compliance and legal teams is that privacy is just one of many regulatory initiatives they face.
However, there is good news as well. Machine learning (ML), a technology to support improved business insight and customer experience initiatives, offers huge potential to help compliance and legal teams accomplish many of their most important rule tracking, employee monitoring and documentation activities faster and more accurately at lower cost.
For example, many legal teams are already using ML to power technology assisted review (TAR) for e-discovery document reviews. With TAR, a machine learning-powered database sits under the document review platform and is trained to do the review by analyzing and “learning from” how a team of human reviewers tags a small percentage of the documents. A certain amount of iteration is required, but once the system is properly trained, it can be significantly faster, more accurate and overall less expensive than human review.
ML can also serve as the foundation for applications that support all aspects of governance and compliance, slashing the time required for key operational processes and leaving time for more strategic tasks. For example, ML-powered applications could:
Machine learning is complex technology, and the potential impact on an organization may not always be easy to understand. To ensure your organization will derive the maximum benefit from the various ML initiatives and better understand how it can support your governance processes, consider the following recommendations.
Ultimately, the value of ML depends on the quality, connection and volume of the data within your enterprise, so the full impact can’t be gauged until you begin experimenting with your data, revealing noteworthy patterns and running proofs of concept.
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