Tackling Regulatory Change Through Automation and Machine Learning

Machine learning and AI technologies are starting to support compliance management functions. The ability to automate resource and data intensive processes is beneficial to compliance management functions struggling with increasing levels of regulatory data. 

Financial services organizations are dealing with a tidal wave of regulatory change that shows little sign of abating. As part of my research for a new study published by Goode Intelligence investigating how machine learning and automation can get regulation under control, I interviewed compliance officers and Regtech experts in both the UK and the US. A compliance officer based in London told me that the financial services industry is facing "mountains and mountains of regulation". This statement is echoed by industry experts including the Boston Consulting Group who believe that "regulation must be considered a permanent rise in sea level - not just a flowing tide that will ebb or even cresting tsunami that will recede."(1)

The combination of information overload and manual regulatory change analysis is creating headaches for many organizations that cannot afford to invest in large specialist compliance teams or automation. The reliance on under-staffed compliance teams to sift through vast reams of complex regulatory data can lead to mistakes – mistakes that organizations cannot afford to make when failure to comply to regulation can lead to financial penalties that can run into the millions, even billions, of dollars. Since the global financial crisis of 2008, banks globally have paid $321 billion for a number of regulatory failings from money laundering to market manipulation. (2)

To reduce the ever-increasing burden on compliance teams, financial service organizations can turn to new regulation change management solutions that automates resource-intensive tasks through machine learning technology.

Just as financial services organizations are increasingly turning to FinTech tools to take advantage of advancements in areas like automation, machine learning and cloud computing, these firms can also turn to the new sector of RegTech to better manage regulation and turn it into an advantage.

Leveraging expert-in-the-loop (EITL) machine learning for automating document frees up compliance professionals to focus their time on the details of actually helping their organizations comply with regulations, rather than just laying the groundwork. 

A smart machine learning compliance solution must offer the following core competencies:

1. Aggregation - from a comprehensive variety of sources
2. Normalization - of millions of documents, citations, rulings and publications
3. Curation & Classification - based on expansive EITL machine learning model foundation
4. Trend analysis - transform raw regulatory data and peer-review trends into distilled insight
5. Personalization and notification - follow specific regulatory topics

I explore this further in a white paper that references the latest Regtech solution from Compliance.ai entitled "Getting regulation under control with Compliance AI".

(1) Global Risk 2017: Staying the Course in Banking / March 2017 published by the Boston Consulting Group https://www.bcg.com/en-gb/publications/2017/financial-institutions-growth-global-risk-2017-staying-course-banking.aspx

(2) Boston Consulting Group February 2017