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Is Artificial Intelligence Ready for Financial Compliance?

Traders Magazine Online News, September 13, 2017

Daniel Fernandez

Like many industry buzzwords, Artificial Intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is – AI has become an overloaded and misused term, often mistaken for Machine Learning (ML). This blog aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today, and highlight how ML can immediately add value in financial compliance applications.

In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Intelligence is the key word. In contrast, Machine Learning refers to a computer system that has the ability to learn how to do specific tasks, and in some instances can use past data to make future decisions or predictions without being explicitly "told" (programmed) how to do so. Machine Learning is a key building block of Artificial Intelligence.

Contextualizing Machine Learning

AI and ML are often confused because the terms are used interchangeably. But they are not the same. Today, ML is used in many narrow compliance applications, including risk detection models, and other event classification use cases. A narrow ML application, however, does not constitute Artificial Intelligence in the context of compliance.

That said, a combination of systems and programs (based on ML) could constitute an Artificially Intelligent System, although no such systems truly exist in the compliance realm today.

Most artificially intelligent systems use a combination of machine learning applications and techniques along with rule-based systems (to be fully interactive). For example, phone-based smart assistant applications (such as Google Now, Siri, Cortana, and Alexa) use a set of application components which are mostly powered by machine learning. These include: language identification, translations, transcriptions, natural language understanding, etc. In these interactions you can perform tasks, such as booking a cab, where the following steps are performed behind the scenes:

  1. Understand your command of requesting a car service and your destination;
  2. Detect your location and determine your optimal pickup location;
  3. Reach a nearby driver and agree with this driver on taking this trip;
  4. Communicate back to you with an estimated fare for final confirmation.

Interacting with a smart assistant in this manner can be considered Artificial Intelligence because the smart assistant fully replaces a human (the taxi dispatcher), who would normally perform these tasks. Thus, human interaction is bypassed altogether.

Why Compliance Needs Hybrid Intelligence

While taxi dispatchers can be replaced by AI, the same cannot be said for compliance analysts. And this is a good thing, because while smart machines and complex algorithms can process a lot of data to automate and perform some human tasks, faster, there are limitations. For example, the current machine learning models and advanced statistical techniques can process far more messages, trades and records than humans can, but today humans are still needed to review, apply judgement and make decisions about what constitutes or does not constitute compliant communications. Why?

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