Big Data & Analytics Heroes: Vince Walden
“You can’t just look at one data source,” says Vince Walden, partner at Ernst & Young, and this week’s Big Data & Analytics hero. With so much data available to us today to get a clear picture businesses must be able to look “at the data in all different angles—upside down and sideways—to get to where we think the issues are in a case.”
How have big data and analytics impacted how you do your job today?
I am absolutely passionate about building the leading, most innovative, anti-fraud, anti-corruption type analytics to mitigate our client’s regulatory and anti-fraud type risk areas. Specifically though, what we found is that combining structured data with unstructured data, such as text, has been a game changer in terms of innovating and improving our anti-fraud type analytics.
How are big data and analytics changing your business strategy?
The issues around identifying targeted analytics for anticorruption, let’s just say for example you can’t just look at one data source, has been a traditional challenge. You have to look at the payments data and you have to look at perhaps traveling and entertainment data. You have to look at third party data such as adverse media or sanctions or watch list data. Again, when you're looking for potentially improper payments that could be conceived or perceived as a bribe, you have to look at multiple data sources and combine multiple analytics beyond the traditional rules based test.
I often hear so much frustration from the client saying "I always feel like I’m reacting to stuff. How do I get ahead of the curve and be proactive and prevent the fraud or the issues from happening in the first place?" Really, that’s where these analytics are aligning.
What "gold nuggets" have you uncovered using big data and analytics?
With the IBM BigInsights platform or the big data platform, we're seeing fundamental differences in how we can process data. It’s almost like data doesn't matter anymore in terms of size. You can throw everything at it.
Having to sift through millions and millions of emails, millions and millions, and, in many cases, billions of transactional data, putting them together and again applying those third party sources (media, adverse media, sanctions and watch list type data, newsfeeds and then perhaps even social media to see what high risk individuals were talking about in their Twitter accounts), all those things come together to paint the picture of the issues around a specific allegation and investigation.
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