Traditional methods of banking are fading out and the rise of smart cash, powered by artificial intelligence is upon us.
Although artificial intelligence (AI) has been around since the 1950s, we are currently riding the peak of the Gartner Hype Cycle. Separating the reality from the hype has therefore become a challenge.
There is no question that machines can automate more human tasks than ever before, but with this promise, there is also a gap between the achievable and the expectation.
There is, in other words, a sense that not much is actually being done by many individual organisations. This suggests that organisations want to use AI and machine/deep learning, but are perhaps not clear on how to do so.
This may be particularly true of the financial sector. Traditional banks and insurance companies are being pressed hard by the rise of new fintechs, which are breaking into profitable parts of the market such as payment provision.
Coupled with deregulation of certain sectors and increased regulation of others, such as data protection, banks are up against the wall in more senses than one.
Identifying challenges
AI BankingEven without the pressure from fintechs, traditional banking practices have their challenges. Consider automated teller machines, or ATMs, otherwise known as cash machines.
Many of us will have experienced the situation where all the ATMs in a certain location have run out of money at the same time. It is annoying for customers to find empty ATMs, but it is also annoying for banks to miss out on the custom, or to have the reverse situation: cash tied up in ATMs that is not being accessed.
Fortunately, from a practical perspective machine learning can and does help. The current focus around AI is generally on automation of manual processes, with the rising prominence of conversational platforms (AKA chatbots), which use cognitive computing power such as natural language processing.
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There are, however, an increasing number of “smart machines,” or machines into which AI capabilities have been embedded to enable them to perform additional tasks. These machines include ATMs that can forecast demand more accurately, and assess the need to replenish.