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.
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.
Contact us to learn more about the future of AI and how you can incorporate it into your enterprise.
Additionally, learn everything you need to take to successfully bring AI solutions to your enterprise. Download our free eBook, The Enterprise Guide to Kicking Off the AI Adoption Process.
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