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Artificial intelligence in financial services

Genady Chybranov , Director, Financial Technology Innovation at Hitachi Vantara | Oct. 24, 2017
Myths vs reality.

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 recognising complex patterns, synthesizing information, drawing conclusions, and forecasting - even better than humans. These are tasks that not long ago we assumed could only be done with a human brain.

As the capabilities of AI have expanded, so has its usefulness in a growing number of fields. According to a McKinsey study (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 PWC1, 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

It is clear that companies must start adopting AI technologies to stay ahead of the pack. In my experience, one of the most useful frameworks for adopting AI technology is the one developed by McKinsey (see chart).

McKinsey successful AI factors

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.

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 percent 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.


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