Smart machines, which encompasses a group of cognitive computing technologies such as advanced machine learning, natural language processing, and prescriptive analytics to make decisions and solve problems without human intervention, have been receiving more attention in recent years due to the rise and success of solutions like IBM’s Watson.
A smart application learns and adapts over time. For example, an application can determine a customer’s credit card balance, the due date, and whether there is enough money in one of the customer’s bank accounts for it to be paid. If there are not enough funds in the account associated with the card, the system can ask the customer if he or she would like to transfer funds from a different account in order to make the payment by the due date. Another example is the use of smart machines in IT operations, where an application can proactively monitor, troubleshoot, and resolve production system issues in real-time.
By 2018, Gartner estimates that smart machines will perform 10% of today’s human work, acting as intermediaries between the organization, employees, and external workers. In financial services, smart machines can be utilized for a variety of functions in areas like underwriting and call centers, and can also help increase accuracy, streamline processes, and, more importantly, improve the customer experience.
Royal Bank of Scotland recently introduced Luvo, a web chat application that is capable of fielding questions from support staff by sifting through a vast amount of data very quickly.
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