I’ve worked in the financial services industry for many years and I know that potentially regulatory penalties aside, no bank wants to be used by financial criminals to further their illegal and immoral money laundering schemes.
I’ve also seen over the years how banks have been early adopters and innovative users of various types of information technology to optimize their transaction processing and record keeping capabilities, and to power their marketing programs. So it’s a bit surprising to me that so many of them are still using largely manual, human-centric investigative processes in their AML compliance departments.
Instead, banks should be using data science-driven technology solutions to turn the tables on financial criminals.
Data science, a long and well-established analytics discipline, employs mathematical/algorithmic tools, multiple statistical techniques and models, data mining, access to big data (internal and external, structured and unstructured), machine learning and automation. It’s forward looking in nature and capable of delivering previously unknown insights, prescriptive information and recommended actions. And it allows data scientists to explore and exploit vast and diverse data stores in a fraction of the time typical of traditional data analysis methods (i.e. seconds or less versus weeks, months or more).
A data science-based, automated AML solution can be much more efficient, effective and cost-effective than traditional, largely manual data analytics approaches. By collecting and examining huge volumes of internal and external data comprehensively and methodically, in real time on a continuous basis, a data science-powered AML solution can find hidden correlations, relationships and activities other AML solutions routinely miss. It can also reduce the incidence of false positives some solutions produce that trigger costly, time-consuming internal investigations.
Interested? Find out more here: http://quantaverse.net/white-paper.