Using Big Data Analytics to Effectively Oversee Financial Markets — The Three Essential Ingredients.

 

Using Big Data Analytics to Effectively Oversee Financial Markets — The Three Essential Ingredients.

Wednesday, November 12th, 2014 - 12:27
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Wednesday, November 12, 2014 - 11:12
Ms. Lori Walsh is the Chief of the Securities and Exchange Commission’s (SEC) Center for Risk and Quantitative Analytics (Center), which looks for potentially suspicious patterns of stock transactions.

The (SEC) protects investors, maintains fair and orderly markets, and facilitates capital formation.  It is organized into five divisions, one of which is the Enforcement Division, where Ms. Walsh’s Center is housed.   Ms. Walsh  says , “the Enforcement Division’s mission is to pursue violations of securities laws and to try to get meaningful remedies, with significant deterrent value.  So identify, pursue, and prevent violations of the securities laws.”

The SEC has several analytics programs that are structured in a “hub and spoke system.” Ms. Walsh’s Center sits at the hub to “centralize the information and determine how to share those techniques and tools.” Ms. Walsh says, the “main part of my job entails proactive identification of … violations of the securities laws.  I focus on data, analytical tools, and techniques to help identify violations more quickly.”

Previously, Ms. Walsh managed the SEC’s Office of Market Intelligence, which receives approximately 20,000 tips annually and are documented, profiled, and then evaluated against several criteria such as credibility, significance, and risk.  Ms. Walsh remarks, “seeing all of these tips come in day after day … made me see a pattern.” From these patterns, Ms. Walsh explores “the data and identifies things before we get a tip.” It is this experience that led her to structure the Center as it stands today.  Ms. Walsh’s methods are to use data mining techniques to identify patterns in the data and correlate them to “violative” activity.

Ms. Walsh shared her three essential elements for a good analytics program.  They include:

  • Data
  • Infrastructure
  • Subject Matter Expertise

Data - The SEC consumes a huge volume of data that is processed, analyzed, and enhanced by regional offices sitting at the end of the various analytic “spokes” referred to previously.  Ms. Walsh sees much potential in the technical advances for data integration.  All raw and derived data consumed at the SEC is collected at the Center.  Data quality is not an issue as Ms. Walsh actively manages it. “A lot of people think we don’t have enough data available.” “I say we’ve got way too much data available to us.” “We’ve got to figure out what data is needed to answer a question.” She goes on to say that “being tripped up by poor-quality data is a slightly different issue.” “You want to get the data as clean as possible.” But then you have to “caveat the output … based on the limitations of the data.”

Infrastructure - Ms. Walsh describes her current integration process as “somewhat laborious and cumbersome.” She reports, “it’s a way of pulling together pieces of the puzzle … and you are able to see connections among the data, putting pieces of the puzzle together.” She is starting to use tools that will not only automate and facilitate the actual integration process, but also “map it for you … using icons or histograms or a timeline so that you can see the data in lots of different ways.” She says that “data visualization is fairly new for us [and] is exciting.”

Subject Matter Experts - Ms. Walsh relies heavily on colleagues who are subject matter experts to provide her with questions to apply to the large repository of data that she shepherds. For example, the SEC’s Division of Enforcement, includes attorneys, accountants, and investigators who are well trained and experienced. Ms. Walsh says “they know what fraud looks like, but they don’t necessarily know how to take that information out of their head and put it into an algorithm or data or analytics.” “We try to get the information out of the experts’ heads, identify patterns, identify data that we can use to apply the patterns to, and then filter the universe of potential behavior to the ones that are most likely to be high-risk.” Ms. Walsh was taught as an empiricist - the first thing you need is a theory to test.

When asked to define success, Ms. Walsh reports, the “ultimate goal is for the Center to be more efficient -- faster at identifying ‘violative’ activity.  And if we identify something before we get a tip in, that’s a success.” She cites an example that occurred recently where the Center used a risk-based analytics process to identify a potentially fraudulent offering. It was referred to an investigative group within Enforcement. Two days later a tip came in on the very same offering. “That’s a success for us.”

 

To listen to Ms. Walsh’s complete podcast and to read excerpts from her interview, visit the “Conversations on Using Analytics to Improve Mission Outcomes” page.

In my next blog, I will highlight the insights gleaned from an interview with Steve Beltz, Assistant Director, Recovery Operation Center, Recovery Accountability and Transparency Board.