Joseph Schumpeter’s theory of creative destruction was first thought up in the mid-20th century. It was a theory that looked at how the continuous creation of technology effectively ‘destroys’ previous technology, suggesting that economies are undergoing a perpetual state of evolution in a system of vertical technological integration. The creation of this new technology is then implemented by profit-seeking businesses trying to gain a competitive advantage. As a result, it changes the usual strategy firms must take in order to remain competitive and survive. The introduction of AI in business and finance industries is now starting another cycle of creative destruction.  

Such cases of creative destruction coming into action is shown by the introduction of the internet and subsequently eCommerce. Easy access to a wide range of products from the convenience of one’s home is already removing high streets across countries at a rate faster than ever. Businesses that don’t adapt their strategy accordingly will struggle to compete and see themselves removed forcefully from competition. AI is now once again shifting industry norms and forcing businesses to change yet again as we see the gradual implementation of AI in business and finance. This is particularly relevant in both the present and ever more so for the future.

 

The Necessity for AI in Business and Finance 

It could be that underestimating the impact AI already has on multiple industries would be a downfall for firms. Whilst the idea behind it may have initially been seen fantastical or excessive 10 years ago, or simply something that was for the niche tech-suave firms, there’s evidence suggesting it’s already past the first stage in playing a key role in firm operations and developments. 

%

of UK C-suite executives must leverage AI to achieve growth

 

A report from Accenture is one such evidence highlighting the importance of AI. Upon research, they find that nearly nine out of ten (84%) C-suite executives in the UK believe they must leverage AI to achieve growth objectives whilst three out of four believe that they’ll go out of business entirely if they don’t scale AI. An article from Forbes highlighting the reports from a Deloitte study also looks at frontrunners in the financial industry that are able to stay a step ahead of the competition by efficient AI implementation. Such pieces of evidence only come to show the impact AI is already having on a variety of industries. It’s come to the point in which the variety of advantages that AI brings aren’t just a luxury but a necessity in order to survive.

 

Implementing AI into Businesses and Financial Firms 

As to how businesses could scale AI into their usual procedures, a blog from Harvard’s Extension School displays this as three key methods: automation, data analytics and various forms of machine learning in order to create NLP for chatbots. It is of course important to highlight that these methods aren’t mutually exclusive from one another e.g. NLP has been used in data analytics to help the data cleaning process. AI can help automate initially repetitive tasks in order to reduce variable costs whilst chatbots can create highly efficient search engines that can be utilized by both workers to help pinpoint key data and consumers in order to increase satisfaction. In addition, the use of AI in cybersecurity has also been incredibly prominent and well implemented by FinTech firms. This is in addition to the use of Chatbots and Blockchain in order to increase operating efficiencies by offering a better point of contact between clients and investors within the CIB sector. However, possibly the one with the biggest potential to bring changes would be AI’s involvement in data analytics. 

Already discussed before in a previous AIBE blog, AI has the power to bring deeper business insights by efficiently organising and bringing together huge quantities of unstructured data for use by employees. One of the most intriguing aspects of using this “big data” is its versatility. If an internal strategist at a top corporation wants to understand insights in how best to manage a firm’s operations moving forward, they can extract information by looking at key performances in multiple departments and possibly conduct a form of regression analysis to find key variables for results. Another scenario could be someone from marketing who wants to estimate the demand elasticity for a new, upcoming product. They could use AI to filter through large quantities of data that looks at sale responses to a variety of similar products. This could then allow them to develop a model that gives insights into what demand they may face with their product and what approach they can take in order to avoid the mistakes of unsuccessful projects.

 

AI in Unilever  

Unilever provides a case study as to how implementing AI can succeed for multinational corporations. Unilever specifically has seen the benefits of AI through its use in big data extraction and recruitment. Their use of 26 data centres across the globe provides a platform for them to gather a large range of both internal and external data to produce insights. The use of AI helps take advantage of machine learning which helps filter through these huge quantities of data in order to deliver comprehensible results for employees to use in future decision making. It’s suggested that they perhaps take advantage of Natural Language Generation to write worded reports on implementations behind the data. 

Unilever then takes a further step forward by using machine learning to clean large quantities of unstructured data. This helps them filter through data that may be more “random” in context to business objectives, where information is available but isn’t presented in a clear, concise why in which AI can sort through. However, forms of machine learning such as Natural Language Processing helps filter through this unstructured information. This gives the company power to unlock the wealth of hidden information that’s hidden behind social media and web articles, allowing them to extrapolate more sophisticated insights for use by teams within a wide variety of divisions. Marketing teams could especially benefit by gaining deeper understandings of consumer preference and selecting more impactful influencers.

 

Unlocking The Power of Machine Learning for Banks and Corporate Finance

Outside of their trading department, banks were seen as more traditional in their use of technology. However, with the huge increase in FinTech firms from the last decade that threatens to use AI to provide a new level in customer satisfaction and efficiency, the industry is now experiencing revolutionary changes as a result of firms looking to remain competitive. A particularly interesting case could be their use of data analytics to increase cybersecurity, as well as reaping increased insights. This is done by AI analysing huge quantities of data from past transactions to spot inconsistencies from fraudulent transactions. This is because transactions often take a variety of steps to prove innocence; machine learning can then spot even the tiniest of actions or suspicious data patterns to identify these illegal activities. This allows banks to remove steps in both compliance and operations in order to speed up transaction times and provide an overall better experience.

Machine learning isn’t just used for cybersecurity. The use of NLP creates chatbots to build powerful search engines and provide highly efficient customer service. JPMorgan in particular has been using NLP in order to provide virtual assistance for smoother transactions. This is especially important in revenue-generating front office operations in which employees are often interacting with clients. A particularly interesting project in which JPMorgan is incorporating these ideas is shown through its Inter-banking Information Network system. Combining both blockchain and AI, the bank has managed to increase the efficiency from the cross-country payments process by allowing multiple member banks to exchange information to verify the approval of payments. This allows the interaction of multiple banks to communicate with one another simultaneously, rather than the usual one-way, bank-to-bank method. This is done by taking advantage of a decentralised information sharing platform that using blockchain can provide whilst using AI chatbots to act as an instantaneous middleman between multiple agents. Speeding up these cross-country payment transactions can be worked for many advantages. A key potential advantage could be that it would allow JPMorgan to raise finance from debt capital markets much easier by pooling together a syndicate of investors at a much quicker rate. This could then help companies undergoing M&A projects to access debt much easier and reduce the time needed to undertake such projects. 

 

Thoughts for The Future and Possible Ethical Considerations

One thing to note about AI is that people can overestimate its capability; they could also under appreciate the dedication needed for it to be fully utilized. The same report from Accenture found the majority of firms had underestimated the time and investment needed to scale AI. This implies that in terms of AI integration into business, most are still at the first stage. In addition, firms need to find a narrow scale in which to implement AI. This means that they need to be specific in how they want AI to improve which parts of the business and integrate it in a way that it matches with business objectives. Simply side-lining it to the IT department or doing one-off experiments won’t give close to the returns that successful AI-integrated firms will receive. 

The next question would be the extent to which jobs are at risk and to what extent it’ll change the future of business and finance. One could argue that increasing AI’s leverage in data analytics could replace jobs as less analysts would be needed to sort through data. Another could suggest that more sophisticated insights would mean companies would need to hire more employees in order to help make more corporate smart decisions. However, what is very likely is that simple, repetitive tasks will be under heavy fire. The development of INN could be one way in which members from a bank’s operating and treasury department could see their jobs lost as AI makes payments become heavily efficient. For the moment, robots haven’t exactly stolen all our jobs and it’s likely they won’t drive heavy permanent unemployment in the future. However, one thing for sure is that this new evolution in technology will change the way in which business is done and anyone who doesn’t adapt will have to fall.

 

Written by Heesang Lee

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