Machine Learning in Finance – now and looking ahead

By Svetlana Borovkova, Head of Quantitative Modelling

‘AI wave is coming’: this catchy phrase opens the recent SAS and GARP survey on Artificial Intelligence in Banking & Risk. The phrase perfectly summarizes the arrival of machine learning in financial services.

What is the difference between AI and machine learning? AI can refer to anything from a computer program playing chess to a voice recognition system such as Amazon’s Alexa, which interprets and responds to speech. AI technology can be broadly classified into three levels:

  • Narrow AI. These are systems designed to perform a single specific (albeit complex) task. Examples are IBM’s Deep Blue, which beat the World’s leading chess grand master Garry Kasparov in 1996, or Google’s Deep Mind Alpha Go, that beat the World’s Go champion Lee Sedol in 2016.
  • Artificial General Intelligence (AGI). These are systems that are aimed to achieve human-level intelligence. Amazon’s Alexa is an example of progress in this direction.
  • Super Intelligent AI. According to the definition of super intelligence, it is an intellect that is much higher than that of the best human brains in practically every field, including scientific creativity, general wisdom, and social skills. This level of AI remains science fiction for now and probably decades more – if not longer.

What about machine learning (ML)? Machine learning refers to algorithms that provide knowledge to computers through data, observations and interacting with the World. In other words, machine learning is the science of getting computers to act without being explicitly programmed. We can also say that machine learning is what powers artificial intelligence.