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Last week we explored a robust approach to AI and showcased a repertoire of AI tools on display within a game of poker.  AI has always been imagined as making our lives easier.  If we are capable of building systems smart enough to perform complex tasks, why not employ AI tools in ways benefitting our lives and society?  This week we’ll highlight a few examples of the practical application of one AI tool, Machine Learning (ML, the focus of blog post #2), in our world.

Machine Learning in Industry

Cyber Security:  In February, the Hiscox Cyber Readiness Report detailed how, in just 2016 alone, “cybercrime cost the global economy over $450 billion.” (Graham, Luke. Cybercrime costs the global economy $450 billion: CEO. CNBC. Web. Feb 7 2017.) Attacks on cyber networks are used to gain access to sensitive financial, medical or personal records across large enterprises.  ML helps customers protect their cyber enterprise by exposing major vulnerabilities through the construction and application of Big Data models that identify “code subtleties in malicious samples.” (Arsene, Liviu. Machine Learning For Cybersecurity Not Cybercrime. DARKReading. Web. Jan 17 2017.)  ML offers the ‘many-eyes’ option:  continuous observation of a system in real-time and the ability to correlate processes with millions of past events to suss out anomalies.  (Magee, Timlin. Machine learning in cybersecurity: what is it and what do you need to know?  Cyberworld UK. Web. Feb 10 2017.)  By aggregating all cyber threats, ML helps analysts to deploy resources to mitigate intrusion and counteract the presence of cyber criminals.

Left to Right: 1. How much does cyber-crime cost the world’s leading 10 economies; 2. Machine Learning is suitable for increasing modern day cyber security challenges

Banking:  The use of ML has greatly transformed the banking industry.  ML sifts through thousands of stock ideas to find those most aligned with a bank’s long-term goals and fiscal profile.  Many banks utilize ML to discern risk attached to loan candidates, and in the process, are able to remove the traditional loan officers.  And all the new features you’ve noticed when using banking apps – everything from instantaneous signature validation to the ability to deposit checks remotely – are examples of optical character recognition, a field of ML.   

Machine Learning used for online banking

Financial Services:  ML has empowered both the serial and casual investor alike, offering stronger, more insightful investment tools and, as a corollary, more diversified money management options and less reliance on banks as the sole provider of financial know-how. Not only are firms leveraging ML to analyze financial Big Data to make predictions and discern ongoing trends, ML is also proving the bedrock of a more personalized customer experience:  investment strategies tailor-made for clients and their priorities.  Entire portfolios are being constructed from intelligent forecasting and insights gained through ML.  Moreover, potential customers are only pitched the goods and services that make both logical and pecuniary sense to their needs.

Machine Learning in Finance

As the examples above illustrate, ML is significantly impacting various industries.  Next week we’ll shine a light on the shortcomings attached to using an ML-only approach, what other AI tools can add value, and how these tools are used to garner improvement in these scenarios.