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Where Will Artificial Intelligence Take Enterprises in 5 Years?

By Advanced Data & Analytics Team | Posted on March 5, 2020 | Posted in AI/ML

Robots are coming for your money.

Not to take it, but to manage it. Artificial intelligence excites some and frightens others. Either way, it will only improve and dramatically shape the workplace and how we interact with financial institutions.

Andrew Brydon, Principal Consultant at Contino, separates reality from hype and defines the state of AI in financial services in his article, The Future of Artificial Intelligence and Machine Learning for Financial Services.

Machine Learning vs. Artificial Intelligence (AI)

People often use machine learning and artificial intelligence as interchangeable terms. Both aim to improve the problems computers can solve on their own, but possess some fundamental differences.

Machine learning (ML) relies on input from a programmer. Computers utilize probabilistic modeling to determine an outcome based on previously inputted data. Machine learning can still solve deep, complex problems. Deep machine learning creates relationships between thousands of data points by layering models on top of one another.

Artificial intelligence (AI) takes information processing even further than machine learning. According to Brydon, the true definition of AI is when a computer passes the Alan Turing Test. The test proves a computer’s ability to exhibit human-like behavior that is indistinguishable from an actual human. Some of the best chatbots already fool humans into thinking they aren’t computers.

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Today's AI Reality

In the financial services sector, reality exists in machine learning over true AI.

Hedge funds powered by machine learning outperform generalized hedge funds. Computers can track financial news and make predictions on stock prices. The article reports some machine learning systems outperform high performing traders by about 2% in overall returns.

Machine learning technology enables financial services firms to solve problems primarily in fraud detection. Fraud detection requires correlation among data sets at a rapid pace to keep up with billions of transactions made each day.

Fraud and compliance services also require firms to re-invest significant amounts of revenue into investigations. Machine learning can help detect fraud by monitoring activity patterns worth investigating warranting human involvement. A customer can then be alerted automatically if patterns trigger a fraud alert.

The Future State of AI

One of the most pervasive uses of AI in the future state will be customer service.

Brydon predicts consumers will interact with AI in financial customer service scenarios almost entirely within the next ten years. Current advancements by Amazon and Google in customer facing technologies are able to turn text into human-like speech.

Most customer inquiries placed in customer service call centers involve relatively simple problems that can be handled by AI. More novel, complex issues will most likely get escalated to a person. This will become a game-changer for financial services companies. It will reduce the cost of customer service at the expense of some jobs, but will allow for an increased focus on other core aspects of business.

Late last year, an article in Forbes highlighted some predictions from IDC and Forrester on artificial intelligence (AI) usage in 2020 and beyond.

Included among those predictions was the idea that, by the year 2024, AI “will be integral to every part” of the companies on the Fortune 500, and that AI solutions will account for 25% of the companies’ overall spend.

As forecasts go, this isn’t very surprising since AI employment is happening across industries. In finance, for example, AI is increasingly powering fraud detection. And in online retail, chatbots and algorithms are being used more and more to enhance customer service.

Another of the predictions in the Forbes article was more daring, however:

AI will become the new user interface (UI) by redefining user experiences where over 50% of user touches will be augmented by computer vision, speech, natural language, and AR/VR. Over the next several years, we will see AI and the emerging UIs of computer vision, natural language processing, and gesture embedded in every type of product and device.

In a way, this forecast for a new UI is also not too surprising. We’re already seeing how AI is changing the way we interact with devices via platforms like Siri and Alexa, and the natural progression to things like computer vision and AR/VR certainly seems likely.

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Not so fast ...

One thing to keep in mind, though, is that while entirely new user experiences defined by AI may be right around the corner, for many enterprises the time frame will likely be much longer.

Why? Because most enterprises—especially legacy ones—are not equipped to make quick turns when it comes to technology. In fact, a lot of companies are still relying on tech from the ’90s in their day-to-day operations.

So, while enterprise AI adoption will certainly increase greatly within the next four years, we expect much of it to be in areas like automation and optimization—smaller projects that return verifiable results without creating a massive disruption.

That’s not to say the UI prediction in Forbes is misguided, but rather, a little too optimistic. The technology will certainly be there, but for many enterprises the need for it won’t yet be there.

Still, there’s no denying AI is going to become further entrenched in the way business is conducted—including UI progressions like gesture-controlled machines.

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Get in now to stay competitive

Regardless of how enterprise AI looks in four years, it’s important for companies to start the adoption process sooner rather than later.

Even little steps like chatbots and automation can go a long way toward improving your business while readying your enterprise for new innovations and opportunities coming down the pipe.

If you’re looking for some help kickstarting your AI adoption, download our free eBook The Enterprise Guide to Kicking Off the AI Adoption Process.

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