Genady Chybranov

Will AI change the face of Intelligence in Financial Services? Myths vs Reality

Blog Post created by Genady Chybranov Employee on Sep 13, 2017

Stephen Hawking has warned that the development of powerful artificial intelligence will be “either the best, or the worst thing, ever to happen to humanity.” But, as an optimist, I’m betting AI will be a net win. Especially in industries that drive the economy, like financial services.


AI is a big deal. Pundits predict that it could contribute up to US$15.7 trillion to the global economy by 2030 –  or more than the current output of China and India combined. And, according to McKinsey1, tech giants have already invested 20-30 billion dollars in AI technology. IBM has its “Watson,” Google has “Deep Mind” and Hitachi has “H”.


And yet despite all the hype, according to IDC2, only one third of companies are planning to use machine learning or other AI technologies inside their enterprises.


Blockbuster benefits

In Hollywood movies, artificial Intelligence means computers or human-looking robots that can learn on their own and make creative decisions in new situations. Modern AI systems are really more like Assisted or Augmented Intelligence solutions which perform specialised tasks. And they can learn and perform some tasks – such as recognizing complex patterns, synthesizing information, drawing conclusions, and forecasting – even better than humans.


As a McKinsey study showed (see chart), early AI adopters are seeing increasing revenues. The biggest benefits are probably in the financial sector, and IDC expects the FSI industry to invest the most in AI/Cognitive systems.

There are plenty of potential applications for machine learning systems in the finance sector. According to PWC3, the highest potential lies in fraud detection and AML (anti-money laundering), front and back office process automation and personalised financial planning. And the benefits can be tremendous.


For example, a brokerage firm is using Hitachi’s H technology to combat fraud. It compares fraudulent data with ordinary transactions to identify patterns that indicate a higher probability of frauds for the trading/brokerage platform.


Start fast

In my experience, one of the most useful frameworks for adopting AI technology is the one developed by McKinsey (see chart).




The starting point should be identifying a business problem where AI can add the most value. The problem doesn’t have to big. Starting fast and unlocking the benefits of using AI as early as possible is more important.


The next step is finding the talent. With a flowering AI eco-system – which includes start-ups, cloud-based tools and research bodies – companies can now find the right resources much more quickly by collaborating, rather building their own data science team.


Source: 4 Steps to Machine Learning with Pentaho


The third step is to identify the right data to build AI models. The main challenge here is to break down the different data silos and put all relevant data together. After the right data has been identified, the remaining 80% of the effort is spent on data engineering.


Once the model is working in a test environment, it needs to be operationalised and integrated with other systems. Data analytics solutions provided by Pentaho, a Hitachi Group company, can automate a sizeable chunk of this work.


Lastly, project results need to be validated against business objectives. Adjustments, if needed, should be made in an agile, iterative and, most importantly, a measurable way.


Don’t get left behind

AI may not yet be capable of solving all of the world’s most pressing problems. But, the status quo of dividing up work between minds and machines is breaking down very quickly.


Companies that don’t step up are going to find themselves at an increasing disadvantage, compared with rivals who are willing to use AI and effectively integrate its capabilities with those of its human employees.




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