Big Data 2Today I continue my exploration of big data in a best practices Foreign Corrupt Practices Act (FCPA) compliance program. Yesterday, I considered what big data is and some ways to think about it. Today I want to move into some thoughts on how to use it going forward. The topic of this series of blogs is based upon an eBook, entitled “Planning for Big Data – A CIO’s Handbook to the Changing Data Landscape”, by the O’Reilly Radar Team, with a series of authors each contributing a chapter. Today I will focus on a chapter by Alistair Croll, entitled “The Feedback Economy”.

Croll believes that big data will allow continuous optimization through what he terms the “feedback economy”. This is a step beyond the information economy because you are using the information that you have generated and collected as a source of information to guide you going forward. Information itself is not the greatest advantage but using that information to prevent, detect and remediate in a compliance program is.

Croll draws on military theory to illustrate his concept of a feedback loop. It is the OODA loop, which stands for observe, orient, decide and act. This comes from military strategist John Boyd who realized that combat “consisted of observing your circumstances, orienting yourself to your enemy’s way of thinking and your environment, deciding on a course of action and then acting on it.” Croll believes that the success of OODA is in large part “the fact it’s a loop” so that the results of “earlier actions feedback into later, hopefully wiser, ones.” This should allow combatants to “get inside their opponent’s loop, outsmarting and outmaneuvering them” because the system itself learns. For the Chief Compliance Officer (CCO) or compliance practitioner this means that if your compliance program is able to collect and analyze information better and you can act on that information faster; you can then use it have a more efficient and more robust compliance program.

Croll believes one of the greatest impediments to using this OODA feedback loop is the surplus of noise in our data; that “We need to capture and analyze it well, separating the digital wheat from the digital chaff, identifying meaningful undercurrents while ignoring meaningless flotsam. To do this we need to move to more robust system to put the data into a more usable format.” Croll moves through each of the steps in how a company collects, analyzes and acts on data.

The first step is data collection where the challenge is both the sheer amount of data coming in and its size. Once the data comes in it must be ingested and cleaned. If it comes into your organization in an unstructured format, you will need to cut it up and put into the correct database format for use. Croll touches on the storage component of where you place the data, whether in servers or on the cloud.

A key insight from Croll is the issue of platforms, which are the frameworks used to crunch large amounts of data more quickly. His most important acumen is to break up the data “into chunks that can be analyzed in parallel” so the data can be considered and acted upon more quickly. Another technique he considers is “to build a pipeline of processing steps, each optimized for a particular task.”

Another important component is machine learning and its importance in the data supply chain. Croll observes, “we’re trying to find signal within the noise, to discern patterns. Humans can’t find signal well by themselves. Just as astronomers use algorithms to scan the night’s sky for signals, then verify any promising anomalies themselves, so too can data analysts use machines to find interesting dimensions, groupings or patterns within the data. Machines can work at a lower signal-to-noise ratio than people.”

Yet Croll correctly notes that as important as machine learning is in big data collection and analysis, there is “no substitute for human eyes and ears.” Yet for many CCOs or compliance practitioners, displaying the data is most difficult because it is not generally in a readable form. To say lawyers are not as proficient as other corporate types in excel or similar tools would be to state the obvious, yet that is about as sophisticated as many practitioners can get. It is important to portray the data in more visual style to help convey the “dozens of independent data sources” into navigable 3D environments. As Joe Oringel is want to say, there is a reason his company is named Visual Risk IQ.

Of course having all this data is of zero use unless you act on it. Croll believes that big data can be used in a wide variety of corporate decision making, from “hiring and firing decision, to strategic planning, to market positioning.” I would certainly add compliance programs as well. But it does take a shift in compliance thinking to use such data. Once again lawyers are particularly ill suited to consider such information for reasons as diverse as training and temperament. This is yet another reason why compliance has evolved to Compliance 2.0, Compliance 3.0 and beyond. Big data allows you to make a quicker assessment of the impact of measured risks. It advocates “fast, iterative learning.”

Croll ends his chapter by noting that the “big data supply chain is the organizational OODA loop.” But unlike the OODA loop, it is more than simply about the loop and plugging information as you move through it. He believes “big data is mostly about feedback”; that is, obtaining the impact of the risks you have accepted. For this to work in compliance, a company’s compliance discipline needs to both understand and “choose a course of action based upon the results, then observe what happens and use that information to collect new data or analyze things in a different way. It’s a process of continuous optimization”.

The three prongs of any best practices anti-corruption compliance program are prevent, detect and remedy. Whether you consider the OODA loop or the big data supply chain feedback, this process, coupled with the data that is available to you should facilitate a more agile and directed compliance program. The feedback components in both processes allow you to make adjustments literally on the fly. For the CCO or compliance practitioner reviewing and analyzing disparate pieces of information available to you, could help you to recognize troubling trends that are not yet full FCPA violations and deliver a solution before you have self-disclose in the new age of the Yates Memo and Department of Justice (DOJ) Pilot Program.

 

This publication contains general information only and is based on the experiences and research of the author. The author is not, by means of this publication, rendering business, legal advice, or other professional advice or services. This publication is not a substitute for such legal advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified legal advisor. The author, his affiliates, and related entities shall not be responsible for any loss sustained by any person or entity that relies on this publication. The Author gives his permission to link, post, distribute, or reference this article for any lawful purpose, provided attribution is made to the author. The author can be reached at tfox@tfoxlaw.com.
© Thomas R. Fox, 2016

1 comments
banabahiscide
banabahiscide

​Traditionally, AML exposure data has been compiled manually by analysts at a handful of companies but things are changing. Here's an interesting post on Quora by Yuval AriavVC, Founder @ Fundbox answering the question - Will FinTech companies be faced with regulatory agencies like banks?

https://www.quora.com/With-the-proliferation-of-Fintech-startup-companies-is-it-likely-for-them-to-be-faced-with-regulatory-agencies-like-traditional-banks# 

Going back to the big data and compliance topic, FinTech has disrupted the finance and banking industry, make it more efficient and customer centric for end users but this has created a gap in regulations. Now, RegTech (regulatorytechnology) companies fills this gap by using advance technologies like machine learning, AI and big data. For example, companies like Nutmeg reshaping the investment industry by creating a market place and allowing everyone easily invest money. And companies like ComplyAdvantage are using big data and machine learning technology to fight financial crime by offering AML exposure data to help compliance officers reduce AML risk.