To AI or not to AI?

To AI or not to AI?

To AI or not to AI?

Machine learning is emerging as the field within AI that is seeing the most amount of real-world applications and use cases among financial institutions, especially in the area of fraud.

Take, for example, a historic event that unfolded in March 2016 that demonstrated the power of machine learning: the victory of program AlphaGo of Google DeepMind over professional gamer Lee Se-dol. This exciting technological breakthrough demonstrates how far AI has come, even in just the past year, and how it is now able to catch humans out.

It's true, though, that the idea of computers learning autonomously has been around for decades. So what has changed as we enter 2017? Why has machine learning gained so much ground in recent years and why does it continue to surprise us?

As we all know, technology predictions over multiple decades are hard to make. According to 1970s forecasts, mankind should have settled the moon and mars by now. But those who extrapolated from the first moon landings could not foresee inflection points like personal computing, the internet, smartphones and the sharing economy.

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Current machine learning technology holds great potential to improve the way humans and machines work together. Machine learning can increasingly free us from many narrowly defined, repetitive, transactional tasks in a steady state.

This enables us to focus on higher-value, complex tasks in dynamic environments. While we used to have to monitor for fraud manually, now machines do almost all of the work.

In recent years, machine learning has gained ground. The technology now exists to deliver enterprise software systems for the financial services sector that can learn how to fully automate business processes at unprecedented levels, react to real-time changes and provide the best possible results for today's digital business.



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