Matthias Dennig/ November 19, 2018/ Uncategorized

The financial sector is one of the first domains to drive interest in using artificial intelligence (AI), even before high computing machines were available. With the release of Louis Bachelier’s thesis Théorie de la Spéculation (Theory of Speculation) in 1900, advanced mathematics in the financial field originated and the rise of statistical modelling marked the beginning of primitive AI in the financial world [1].

Since then AI has seen two boom-bust cycles: in the late 1970s and in the early 1990s.
1993 FinCEN Artificial Intelligence system (FAIS), sponsored by the U.S Department of Treasury could already be used to determine incidents of money laundering [2]

Nowadays, AI is booming again, particularly in the area of risk and compliance. According to AUTONOMOUS 2.5 million financial service employees are already exposed to AI technologies in front, middle and back office in the US alone. This is not surprising, because banking includes huge data sets, which lends itself to a computer-assisted analysis. In addition, compliance is faced with big new challenges: Increasing regulatory complexity, consumer expectations of real-time products and rising costs due to labor intensive work as well as steadily increasing use and variety of digital products.

What is artificial intelligence?

Artificial intelligence is the technology by which a machine can learn on its own, rather than relying on fresh programming code from a human counterpart. While often compared to fanciful interpretations in science fiction, AI in today’s world, and especially in a compliance function, is a far, far more primitive thing.  When we speak of AI in a compliance context, what we are generally speaking about are data management programs that can analyze large amounts of data quickly, make key correlations between different pieces of data to draw pre-programmed conclusions (such as raising a red flag on a certain kind of transaction), and to begin to draw new conclusions based on the data it sees, subject to human interpretation and approval.

By applying AI to compliance, KYC/AML, authentication and other forms of data processing banks and credit unions could save a staggering $217 billion [3].

Specific technologies that can benefit from AI are:

  • Authentication Biometrics – Biometrics use the unique physical attributes of an individual to connect them with their identity
  • Monitoring – ongoing monitoring of company communications, financial transactions, vendors, brand reputation, and employee biometrics
  • Anti Fraud and Risk – Compliance analysis is shifting from examining a selected sample of all transactions in a batch process (e.g., 5% of a month) to a continuous AI evaluation of every single transaction in real time
  • KYC/AML – Tying identity to a KYC/AML compliance process at scale is being accelerated by AI technology
  • Complex Legal and Compliance Workflows – Legal documents can be analyzed using machine learning to transform them into structured data and compare the differences

According to Accenture 77 percent of banks plan to use AI to automate tasks to a large or very large extent in the next three years [4]. It seems that AI and compliance are in it together for the long haul.


[1] District 3, ‘The history of ai in finance accessed 5 November 2018

[2] Golberg et al (1995) The FinCEN Artificial Intelligence Systems: Identifying Potential Money Laundering from Reports of Large Cash Transactions: accessed 5 November 2018

[3] accessed 5 November 2018

[4] accessed 5 November 2018

Matthias Dennig

About Matthias Dennig

Matthias Dennig works as a presales and consulting expert for the product SMARAGD aces360. His area of responsibility includes advising prospective clients and supporting the introduction of the SAP BIS based product SMARAGD aces360. For more than 15 years he has worked as a project manager in the compliance environment for targens GmbH in various major projects in Germany and other countries.