Fraud is a crime that is as old as human history that can have devastating global, national, institutional and personal consequences. Numerous laws and regulations are written, and regulators work diligently to detect and prevent fraud. As it is said, best fraudsters are the ones who are not yet caught, and regulators are always slow to catch up.
This may be true in general, but the reality has dramatically changed in the financial sector recently: For example, now U.S. Securities and Exchange Commission (SEC), the financial regulator of the US markets and institutions, is significantly advanced technology-wise and far ahead of the private sector.
SEC examines thousands of firms every year, including the 13,000+ Registered Investment Advisors (RIA) and 4000+ Broker Dealers and thousands of other financial firms. When the SEC examiners conduct their audits, they can ask for all of their business related data: all financial transactions, employee personal trades, instant messages, portfolio construction methods, human resources records, client lists… Practically speaking, according to the US laws, SEC can ask for anything and the registrants have to provide all of those.
In the past, SEC did not have the technological tools to analyse this data: hence they asked for only limited data sets and most of the examinations relied on sampling a fraction of data and almost everything was done either manually or in an ad hoc manner. Despite their hard efforts and diligence, SEC, devoid of technology, could not fulfil their responsibilities to their satisfaction. As a consequence, the executives at the registrants did not feel the need for technology’s aid in their compliance efforts. They relied on simple tools or did everything manually: just sampling few transactions or e-mails checked many boxes.
The 2008-2010 financial crisis changed all that. Regulators came under fire; new staff were hired and considerable sums were spent on building technical infrastructure. As part of these efforts, Dr. Kurtaş joined SEC in 2010 and founded the Quantitative Analytics Unit, first Data Science team of Office of Compliance and Examinations (OCIE) at SEC, in 2012.
During his tenure at SEC, Dr. Kurtaş led many transformative projects including the National Exam Analytics Tool (NEAT). Starting around 2014-5, NEAT was deployed in the examinations and inspections of all the registrants. NEAT allowed examiners to ask for many years of financial transactions in the form of trade blotters, all employee trading activities, and other sets of data. Examiners now could combine these huge data sets coming from various sources with news, market data and other sets of data, and identify potential violations in a matter of minutes, not hours! It also gave SEC the tools required to look for complicated violations like insider trading and market manipulation proactively.
Suddenly the SEC was armed with a nuke, and the compliance staff at the firms it regulated or compliance consultancy firms who serve these firms were way behind.
As in every disruptive change, it takes time for everyone to understand and appreciate the scale of change that rules the new game.
The questions for the compliance staff at the firms registered with SEC in USA and other regulators elsewhere or the compliance consultancy firms who serve these firms are:
As Bigdata Teknoloji, we made it our core mission to help the compliance staff at the regulated firms or compliance consultancy firms and use technology to do their job better, faster and more effectively. This will help creating a more efficient, fair and transparent financial system.
Our Intelligent Compliance Analytics Tool (iCAT) is designed to provide a smart decision support system for the compliance staff. iCAT is not designed to eliminate compliance staff; on the contrary, iCAT will help them to do their job in a much more efficient and fun way.
iCAT is built on the following key principles:
iCAT processes data from all kinds of sources; financial transactions, portfolio holdings, employee personal trades, e-mails, instant messages and chats, social media, news, voice recordings, and market data, to name a few.
iCAT uses Machine Learning, Natural Language Processing and other Data Science and Analytics techniques.
iCAT is not built for engineers or rocket scientists: it is as intuitive as a smart phone that a 75-year-old or a 5-year-old can use.
iCAT is a disruptive aid that combines human insight with machine speed and accuracy. It is affordable by design. No reason for high technology to be outrageously expensive.
We are piloting iCAT in the US now.