Picture these scenarios: Co-workers email each other about their secret competitive new business. A product manager who was passed over for a promotion exposes his company’s engineering plan on a social media site. An ex-employee took plans for a new product to a competitor.
This same scenario repeats itself in businesses all over the world—putting companies at risk. The issue is compliance plus cost: the need to protect your company against the high financial risk of non-compliance, while simultaneously avoiding overspending on compliance solutions.
It may be too late for these companies, but not for you and your constituents if you are smart about using data analytics.
Controlling the Risk The largest financial threat is a successful investigation ending in expensive fines and customer loss. The Association of Corporate Counsel surveyed over 1300 in-house counsel in 41 countries about their top work concerns. The GCs’ priority concern was complying with governmental regulations and cross-border requirements. In the survey, one third of General Counsels reported that regulators had targeted their companies within the last two years. The remaining GCs indicated that it was just a matter of time until they were also targeted.
If keeping up with regulations is painful, the price for not doing so is worse. Corporations may lose hundreds of thousands to millions of dollars not only in fines, but also in lost business rising from negative press. Reputational hits and customer lawsuits can be massive.
Cutting the Cost Legal and compliance teams are tasked with protecting the enterprise against severe losses. However, the price tag of ongoing compliance is already expensive. Corporate data is growing fast, and potentially non-compliant activities may occur all over the enterprise. Many compliance departments can only do so much with current budgets and staffing levels, and noncompliant activities can go unreported for months and years.
Take the recent embezzlement in South Korea, where a European company’s employee allegedly absconded with $100 million. If this company had implemented a data analytics compliance platform, there is a much higher chance it could have proactively identified financial irregularities in the South Korean office.
The Analytics Safeguard The key to improving compliance and cutting costs may reside in new data analytics approaches that augment existing litigation and traditional enterprise risk management tools to better detect and pinpoint signs of risk. Advanced analytic platforms are capable of consolidating massive data from multiple sources, both structured and unstructured, from different vendors and data vaults. It applies up to billions of prior compliance classifications made by human experts, and proactively recognizes non-compliant data (and patterns) as they are happening.
Data analytics offers tremendous opportunity to pinpoint risk while avoiding compliance overspend: once a lesson is learned, the algorithms never forget. Currently, the difficulty in re-using past analysis and decisions across legal and compliance matters leads to inefficient processes, significant replication of work, inconsistent analysis, and wasted money and time. Highly specialized analysts also limit the ability to identify patterns of non-compliance across the organization. Finally, these traditional approaches are reactive in nature – based on past events (like when it’s too late, and litigation has already hit). If current staff do not know about suspicious communications or what they’re looking for to detect signs of non-compliance, then they will not know to run an analysis–and the risk continues to lurk.
Here’s the bottom line: the platform’s machine learning, automation, scalability, and high performance yield up to 60% cost savings over traditional compliance review methods.
For Example, Keeping up with the SEC In 2016, the SEC took on 868 cases, 63 more cases than in 2015 and over 100 more than in 2014. Its investigations yielded orders and judgments in the amount of $4 billion.
A large part of the SEC’s new track record? Data analytics to locate suspicious transactions and identify patterns from collected data.
If the erring organizations had been using data analytics, they very well might have detected the patterns themselves. Armed with this knowledge they could have remediated the problem before the SEC got involved.
Keeping up with the myriad regulations has always been a hard job, and it’s getting harder thanks to massive data growth and tightening regulations. Corporations can change the game in their favor by using data analytics to proactively analyze and remediate non-compliance. And they can do it at significant savings over manual procedures — not to mention potentially saving millions of dollars over the lifetime of the solution.