Data Combing Software Could Preempt Next Banking ScandalAugust 9, 2012 3:20 pm ·
Following this summer’s LIBOR banking scandal involving Barclays, wrought with such institutional and governmental corruption that it almost seemed fictional at times, it doesn’t come as much of a surprise to see a renewed focus on the source of some of the scandal’s most shocking moments—electronic communications. As the controversy unfolded, it was discovered that everything from emails to phone conversations—and even instant messages—were used to carry out the historic swindle.
One such response, as a recent article from MIT’s Technology Review reveals, has come from a company by the name of Digital Reasoning, who hopes that an intuitive program it has developed can help prevent another LIBOR-like scandal from happening in the future. By using “machine-learning software that raises red flags found in messy, or ‘unstructured,’ text data, including e-mails, tweets, and documented files,” writer Jessica Leber explains, this program could potentially alert banks to such manipulation and collusion before it becomes problematic.
In the past, Digital Reasoning has worked primarily with military and intelligence agencies, offering its data combing software as a means of organizing large swaths of information from multiple sources into clear, actionable intelligence. Now, given the daunting amount of information that is transmitted in the financial industry, the company believes it has found yet another appropriate application for its software.
And if you ask CEO Tim Estes, the industry’s need for Digital Reasoning’s service is “pressing.” In fact, he and others argue that financial institutions lag significantly behind other industries in terms of data analysis technology.
Unlike text analytics programs developed by companies such as Microsoft, IBM and Oracle, which operate based on predetermined commands and require people to sift through insurmountable amounts of data, Digital Reasoning uses a different approach that purportedly provides more useful data in less time, purportedly saving companies billions.
Leber’s explanation continues:
Digital Reasoning’s software uses training algorithms to read and assemble masses of e-mails and other text data, and then sorts the words and sentences into organized relationships that can be compared in context. This data can be searched, or alternately, be used to trigger preset alarm bells automatically.
Given its intended purpose to pinpoint and prevent fraud, insider trading and other questionable, if not outright illegal activities, Digital Reasoning’s software certainly sounds promising. However, given how widespread such activity can become, it’s easy to see how this software could be used to further evade institutional responsibility by pinning systemic malfeasance on a few expendable individuals.
In fact, what’s to say that such “preset alarm bells” couldn’t be used as a way to silence potential whistleblowers looking to disclose potentially damning information?