AI has become ubiquitous in our day-to-day lives and professions. Perhaps you’re used to hearing old-timers complain about how millennials are glued to their technology. Why not embrace it? Recently, technological trends such as blockchain, the internet of things (IoT) and the rise of cloud computing emphasise that having a sound knowledge in these areas may give you a competitive edge in your career . What’s more, some of the most successful companies have increased the use of AI in recent years. According to global management consulting firm McKinsey, In the past 15 years, Amazon, Google, Baidu and other successful entities have leveraged up on machine learning to obtain sizeable commercial advantages. If you would like to know more about these commercial advantages specifically, you can download and read the article yourself from .
The intent of AI is to mirror the human brain like with the way that humans learn. It is fed large amounts of information and subsequently processes the data. As information is digested, AI improves through repeated exposure to recognising patterns. Building upon those patterns to forecast is essentially where the push of AI is directed. Think back to basic regression models within your econometrics classes. The core of AI is like the use of independent variables to forecast an effect on a dependent variable.
Mentions of AI were first documented by British polymath, Alan Turing in the 1950s. However, when Turing proposed the notion of AI, he faced a major constraint. At the time, computers were unable to store instructions given to them by humans and could only execute commands. In addition to these constraints, the costs of leasing computers at this time were peaked. Hence, only the most prestigious universities had the privilege of furthering research into this field.
From 1957 to 1974, AI became stronger. Computer accessibility increased and machine learning algorithms provided better tools to apply and boost understanding of AI. Skipping forward to 1997, the power of AI was exposed to the world through chess grandmaster, Gary Kasparov and IBM’s AI, Deep Blue match. Chess playing AI, Deep Blue defeated Kasparov. This marked an unforgettable moment in history . That was the first time a reigning world chess champion was defeated by a computer. This is one event that served as a catalyst to promoting the strength of AI. How is AI different from its genesis? Along this timespan, the notable distinctions of AI from the 50s till now has been memory and speed. Both work in conjunction to improve AI as we see it evolve decade by decade.
Above driverless cars, robot interaction and other automations, there is much potential for AI to change the face of banking. AI has placed greater demand for the construction of AI labs and incubators . To support this change, Chief AI Officers are making larger investments in technology driven changes including AI. A 2019 survey found that CFOs had upped their implementations of technology in general. Blockchain was cited as implemented by 22 percent of CFOs up from nothing in 2018. Advanced analytics had increased by 24 percent as well. Most importantly, AI was up the highest with 25 percent of total finance technology investment .
Moreover, the technology industry remains a key driver to the American stock market. This highlights the importance that AI pertains to in the finance and banking sector. Within banking, AI has permeated itself in the form of chatbots. IT management company Gartner predicts that by 2020, consumers will manage 85% of their total business interactions with banks through fintech chatbots. The impact of this technology is being positively embraced by banks as a cost-saving method. Juniper estimates that by 2022, chatbots and virtual assistant will save companies $8 billion per year . Not only that, being able to filter out inquiries that can be resolved with less human capital, means that there is greater efficiency. Additionally, human professionals can devote their expertise to more intricate areas and prioritise certain customer inquiries. AI is able to execute repetitive, mundane tasks with greater efficiency than humans. Since AI does not face the same emotional and physical constraints as humans, diminishing marginal returns to labour has a reduced effect . This is why companies find the move towards AI a lucrative opportunity. Not only confined to the banking industry, AI may be the platform that drives customer insights as a learning device of customer wants, patterns and behaviours. A prime example of this is flu tracking. Research was conducted to predict the number of case flus based on Google search data .
A ramification of the Global Finance Crisis in 2008, is that more stringent requirements of credit assessment were introduced. Credit supply is a crucial component for driving business within banks, and a pivotal component of supplying credit is the assessment of individual creditworthiness. Credit assessment is generally affiliated with the need to digest large quantities of prior lending history. AI sees much potential here because it has the capacity to analyse data from varying sources together to generate coherent decisions. Hence, many institutions in India already see the use of AI day-to-day when assessing creditworthiness.
While AI appears to have a promising future across several sectors, AI doesn’t suggest complete reform of the entire employment industry. According to EY, jobs are likely to be reshaped rather than replaced as banks retrain their current employees to be proficient in understanding AI. Moreover, Accenture predicted that banks that wisely incorporate AI will see a 14% increase in jobs . This doesn’t mean that you should go diving headfirst into a career path strictly dominated by AI. While AI has potential, it is not without limitations. For example, the development of AI is contingent on masses of expenditure. Getting the best technology isn’t the cheapest investment a company can make. Limitations to AI development during the 2000s was attributable to dried up funding as a result of the dotcom bubble burst. You can learn about the dotcom bubble burst of the 2000s here .
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