A few weeks ago, we discussed how companies are increasing their investments in enterprise governance, risk and compliance, largely in response to an uptick in regulatory action and enforcement. Financial services firms, particularly, are struggling to comply with an uptick in regulatory demands from a growing list of entities, including FINRA, the Securities and Exchange Commission, the Office of Compliance Inspections and the Commodity Futures Trading Commission.
Earlier this year, FINRA issued a $17 million fine against a financial services firm for anti-money laundering compliance failures, the largest penalty ever issued. As such, financial institutions are reevaluating their strategies for detecting potential compliance infractions before they become a liability to the company.
Below are best-practices strategies, outlined in Investment Advisor’s recent article, to manage compliance risk:
- Consolidate data to gain a holistic view of data across all legal and compliance matters.
The article notes that “Over the years, financial firms have collected and reviewed documents for investigations and lawsuits from data sources that are typically within the scope of a FINRA investigation, including email, chat and social media. The problem is most legal and compliance teams view each matter individually, leading them to reinvent the wheel each time a new investigation or case arises.”
Next-generation analytics to manage Big Data, however, are capable of consolidating billions of prior data classifications across documents from prior litigation, investigation and compliance matters into a single repository, allowing legal and compliance teams to repurpose past work to identify trends. By predicting privilege, non-responsive, trade secret and other coding, this type of analytics helps lawyers eliminate multiple reviews of the same documents across cases, improve quality and save up to millions of dollars per case.
- Use predictive analytics tools.
Big data analytics platforms are also being used by innovative firms to detect trends in data that serve as an “early warning” system, enabling the organization to redirect or remediate employee behavior before it turns into a liability. Developed by subject matter experts and data scientists, customized algorithms are designed to capture behaviors of interest and flag such documents that indicate risky behavior.
- Incorporate traditional eDiscovery document review approaches.
Traditional analytics approaches shouldn’t be scrapped in favor of a Big Data analytics platform. They can be continued to use in conjunction with next-generation analytics platforms to detect risk of FINRA non-compliance. These include keyword searches and pattern detection techniques, such as predictive coding algorithms, data visualization tools, concept clustering, linguistic analysis techniques and anomaly detection approaches, that can rapidly cull and organize seemingly meaningless documents into patterns that firms can use in eDiscovery.
By using proactive analytics to predict potential FINRA-related compliance risks, legal and compliance teams can gain holistic insight into their data which can not only detect indicia of risk behavior before FINRA comes knocking, and save substantial costs as well.