Financial services firms must comply with data mandates from a growing list of regulatory entities, from FINRA and the Commodity Futures Trading Commission to the Office of Compliance Inspections and Examinations of the Securities and Exchange Commission.
Amid this increasingly global regulatory environment, firms are struggling to comply with this vexing array of demands on their data. The proliferation of big data combined with changing rules and short response times create a perfect storm. In response, legal and risk management teams are increasingly using their corporate and employee data to their advantage to reduce or avoid legal fees and fines.
In May, FINRA issued a $17 million fine — the largest-ever penalty against a financial services firm — for anti-money laundering compliance failures. Financial institutions hoping to avoid a similar fate realize that a backward-looking approach to compliance cannot address future liability, especially now that governmental agencies are employing sophisticated data analytics to identify illegal activity.
Forward-thinking firms are mining their existing data for actionable intelligence to manage investigations more efficiently and cost-effectively, as well as stay ahead in managing compliance risk.
Consolidating Data to Reduce Manual Reviews
Case-by-case analytics are powerful tools, but they pale in comparison to the might of a holistic approach. 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.
Newer analytics platforms with predictive capabilities, however, can aggregate billions of prior data classifications across legal and compliance cases from multiple repositories, allowing counsel to repurpose and view their past work in the clearest possible context. By aggregating data into a single repository, corporations and their counsel, in conjunction with data scientists and analytics experts, can spot trends across all prior and current matters. The platforms’ ability to predict privilege, non-responsive and other attorney designations eliminate multiple reviews of the same documents, improving efficiencies and saving millions of dollars per case.
Using Predictive Analytics to Detect Risk
Financial services firms do not need to await a FINRA sweep to use predictive analytics tools. These analytics can also be used to uncover trends in data that can serve as an early warning system, allowing an organization to redirect or remediate employee behavior before a FINRA or other regulatory investigation commences. Take, for example, a firm that wants to proactively identify behaviors that raise potential legal or regulatory concerns around employee misrepresentation relating to an investment. Predictive analytics, with customized algorithms designed to capture behaviors of interest, can flag documents that indicate risky behavior or language, allowing the firm to redirect employees and avoid formal disciplinary action.
Incorporating Traditional Data Review Approaches
While more sophisticated, big-data technologies and techniques exist, traditional approaches to data have stood the test of time. Financial services firms can continuously refine them to help with FINRA compliance. These include:
Keyword searches. Unstructured data is a prime source of risk intelligence for financial services firms. It’s no secret that by using well-thought-out keywords, firms can search data for indicia of risk and troubling patterns that may indicate red flags. To maximize their value, keywords must be revisited for accuracy. If your data from past FINRA examinations is aggregated in a single repository, it allows you to test results over time. Too often, in an effort to be fully compliant, search terms will be added to a list without any thought of removing or augmenting terms that have consistently yielded low responsive rates. The result is that the firm pays outside counsel to review data that an intelligently deployed search process would have excluded.
Pattern detection techniques. Advanced data analytics tools and techniques can rapidly organize seemingly meaningless data into patterns that financial services firms can use as the cornerstone of a pre-emptive risk management strategy:
- Anomaly detection tools can mine records for evidence of anomalous transactions.
- Linguistic analysis techniques can recognize illicit discussions that seem innocent.
- Concept clustering can detect hidden patterns within documents.
- Data visualization tools can highlight irregular transactions that indicate risk-laden dealings between an employee and third party, such as unusual travel and entertainment expense reports.
- Predictive coding algorithms help organizations quickly mine for the gold in their data sets. Algorithms study senior lawyers’ coding of a sample set of documents, refine its logic iteratively, and then extrapolate human experts’ reasoning across an entire document population.
Financial institutions face an overwhelming burden of compliance in the vortex of constant regulatory change. To avoid drowning in a sea of reactive activity, financial services firms should consider moving to a data-driven analytics strategy. A proactive analytics strategy can deliver unparalleled insight into their data, which can drive better efficiency in future matters by detecting and resolving potential risks.
— Read Data Security Best Practices for Financial Advisors on ThinkAdvisor.