The big change now is that we’re moving rapidly from a world in which machines automate heavy lifting and ‘muscle work,’ to one in which algorithms conduct tasks requiring some level of cognitive skills – much like humans.
Given these changes, over the next few years financial institutions will apply an increasingly diverse range of algorithms to a broader set of service offerings, unleashing unimaginable efficiency gains, although these gains will be tempered by the inevitable upheaval from some jobs becoming redundant.
As ever, changes will occur at a breath-taking pace. For evidence of this pace, look no further than the recent history of Nokia
and Apple. In 2007, Fortune magazine crowned the then telecommunications giant Nokia “the king of cell phones” on
its front cover; meanwhile in California, Apple released the first generation iPhone. Come 2018, the market value of Apple crossed the US$1 trillion mark, iPhones are one of the dominant icons of the tech revolution and Microsoft (which acquired Nokia in 2014) has written off most of the acquisition price it paid for Nokia.
A key lesson from this is that the future, especially when it comes to technological advancements, is rarely linear, so it would be prudent not to take all of today’s extrapolations into the future as a given. It took only eleven years for Nokia to go from the undisputed king of mobile, to being completely usurped by new entrants like Apple and Android phone makers.
MORE INVESTMENT, MORE ALGORITHMS
The key ingredients of AI innovations are (1) data; (2) computing power; and (3) algorithms. Data in many ways is the hardest part as it tends to be inherently flawed and based on the structural biases from the past. The need for better, and cleaner data and more computing power requires significant amounts of capital.
To get a sense of where we’re headed in the next few years, it’s helpful to first look at the numbers.
The World Economic Forum, in a recent publication, estimates at least USD $58 billion in global AI investment by 2021, including USD $10 billion from financial institutions. These estimates represent a 48 percent CAGR in global cross-sector AI investment over that time-period.
As investment increases, the range of algorithms used for commercial purposes will also expand accordingly. There are two major groups of algorithms to consider: ‘supervised’ and
‘unsupervised.’ Supervised means input and output variables are clearly defined; the algorithms map patterns to make predictions within predetermined parameters. For unsupervised algorithms, outcomes are not clearly defined; algorithms are left on their own to decipher and decode trends in large data sets.
The following table sets out the different types of algorithms and some sample real life applications.