Wednesday, February 3, 2016
I’m at Legal Tech New York this week, meeting with Compliance Officers, litigators, and legal technology specialists from all over the U.S. and Europe. LTNY is arguably the industry’s favorite conference, consistently offering insightful, engaging panels and great opportunities to meet industry thought leaders and discuss the latest developments affecting global business and litigation. Often – at least for me – the conversation gravitates to Riskcovery, Altep’s portable compliance risk assessment platform.
Here is a summary of some interesting discussions I’ve had at LTNY so far:
Do the mobile connectors work with any kind of chat system? We need a better way to assess data on Facebook, Whatsapp, LinkedIn messages – that kind of thing.
The short answer is yes, with some limitations. If you can export a delimited file of the chat logs, it can be ingested into Riskcovery. Most social media messaging systems don’t cache data on the phone or device, so you’d probably have to look to the chat app server or the user’s main account page in order to find useful data – and many platforms don’t provide a clean way of exporting. That being said, our team is well aware how important SMS messages, chat data, and other forms of social media data are becoming, in both litigation and compliance, so we’re constantly working to expand our ability to deal with those data types in a meaningful and valuable way.
So how does Riskcovery’s concept search feature work? Do you mean you search using a narrative? Like prose?
In a sense, yes. Think about classic keyword searching. No one is going to call a bribe a bribe, so if I’m investigating a situation involving kickbacks, searching for “bribery” isn’t going to help much. However, if I can search for passages of text in which the elements and activities that typically surround bribery are discussed, my efforts are going to be more fruitful. So, I start by identifying conceptually-rich descriptions of the issues I’m interested in, and then I develop a taxonomy made up of my conceptual samples, arranged in a logical folder structure. When I process the ingested data population, Riskcovery essentially uses those concept samples as search queries; any document that is conceptually similar to a sample is added the appropriate folder in my taxonomy, for further review.
What taxonomies are available?
We’ve assisted in a number of investigations involving the Foreign Corrupt Practices Act, and we’ve also used Riskcovery to identify instances of sexual harassment and inappropriate communication within the workplace. In one engagement, we helped a regional medical center identify a former employee’s violations of the Stark Law – that was interesting because it turned out there was more going on than the client was even aware of. The takeaway is, we can customize a taxonomy to target any concept of interest, using the client’s materials, case documents, articles from the internet – pretty much anything that describes what we’re looking for.
With another day and a half left at LTNY, I’m sure I’ll have many more great conversations about Riskcovery and other tech innovations – if you’re around and you’re interested in talking with me about Riskcovery, you can schedule an appointment online, or if you’d like to see a demo after things calm down next week, I can set something up via WebEx – just send me an email with your preferred day and time.
Subscribe to receive our
Experts’ Insights Blog feed.