Literature Review: The Challenge Of Artificial Intelligence (AI) And Cloud Technologies

Introduction

The area of study or the research is based on the challenge of Artificial Intelligence (AI) and Cloud Technologies as encountered in the Finance Team or Function. Initially the whole of the research topic to find out if there was study done on the challenge of artificial intelligence (AI) and cloud technologies in the finance function was used. A preliminary literature review was therefore carried out to get a sense of what existing literature says, as well as identifying any strengths, weaknesses and gaps in that existing scholarship. A selection of not less than twenty scholarly articles were reviewed and this paper discusses issues emerging from them. As part of the literature review process, scholarly articles were accessed from the university library smart search, using a combination of key words of my research area.

From my analysis, it became apparent that scholarly material directly linking AI, cloud technologies and the finance function in business practice is relatively scarce. However, the journals found were that dealt with Artificial (AI) and cloud computing as separate concepts and areas of practice. There was no single journal directly dealing with cloud technologies as it relates to the finance function in different organisational contexts. Then google scholar provided on the university online library was used to widen the range of scholarly material to be incorporated as part of the literature review for this area of study, and this yielded some materials which were then used.

Research Literature Review

The information technology has been in use in most of the business processes in process re-engineering designed to enhance capabilities to create a competitive edge against competitors (Attaran 2004). The literature review on the challenges of artificial intelligence (AI) and cloud technologies in the finance sectors to deal with the need to create a united platform like crypto currency and how to tackle targeted hacking legally (Erdelyi & Goldsmith 2018). The automation of business functions must fit the chosen organisation business model. This is based on the global business calls that Artificial Intelligence and Cloud Technologies are now considered globally as revolutionary concepts.

Conceptual Framework: Artificial Intelligence, Cloud Technologies and the Finance Function

It is important to provide a conceptual framework on which this study will be premised. A number of key concepts need to be defined and explained in the context of this research project. One of those key concepts is the idea of artificial intelligence (AI) itself. There are many definitions of artificial intelligence in existing scholarship on the subject. There has not been a rigorous conceptual link between AI and cloud technologies in the context of the finance function in business organisations. This aspect of literature review aims to contribute in that respect. Much of the scholarship conducted earlier mainly centred on AI as part of computer sciences, designed to create computer intelligent machines that are capable of planning, reasoning, intelligent learning and solving any problems presented to them the same way humans handle similar situations. Although this is an important contribution to scholarship, this study seeks to go further and illustrate how AI influences the finance function in business organisations. One of the key challenges of AI in relation to the finance function, is to do with how to capture that Chatbots used to solve customers’ problems into invoiceable services the business can benefit from at a later stage in the business process.

Another important concept in the theoretical foundation of this study, is the idea of cloud computing. The preliminary literature review I have carried indicates that there are gaps in scholarship around this concept, particularly as it relates to the finance function in organisations. A brief examination of literature suggests that most existing scholarly work on cloud technologies centred on cloud computing predominantly from a computer science perspective. There seems to be a limited attempt to have literature that deals with cloud technologies as it is adopted and applied in the finance function. At the same time, not much scholarly work has been done to link cloud technologies and AI on the one hand and the finance function on the other. This is essentially what this study seeks to achieve.

There is no universal agreement among scholars and practitioners on what cloud technologies (particularly cloud computing) are. In other words, it is a concept that is talked about by business researchers and practitioners, than it is understood. One useful starting point in trying to understand what cloud computing is and how it relates to the finance function, is the idea of grid computing. This refers to the use of widely distributed and interlinked computer resources, infrastructure and associated software in pursuit of a shared goal within organisations (Reese, 2009). An attempt has therefore been made by scholars to define cloud computing in relationship to grid computing as they apply to organisations. This conceptualisation is helpful to the extent that it illustrates how cloud services are used by end users across spatial distance (Reese 2009). The diagram below illustrates one of the ways of thinking about the link between the three concepts – AI, cloud technologies and the finance function:

The diagram above was adapted to suite The Author’s Definition

One of the challenges finance teams in most organisations, however, relates to how to establish a vital safe link between the AI and Cloud Technologies used in their respective organisations. In order to supplement the literature review in terms of addressing adequately the research questions set out in the research proposal, this qualitative study will use action research which involves practitioners, to generate new knowledge which can be reliably tested.

The challenges facing finance teams in relation to implementation of the right automations or digitalisations and cloud technologies could conveyed through virtualisation and scalability (Foster et al (2008). The challenge of the finance departments of many organisations is to reduce the risk implementing the right AI and is stored safely in the cloud to enable their employees or sub-contractors to work remotely from the offices. The IT resources dynamic scalability must allow restricted access to cloud as commanded by the Artificial Intelligence (AI) to mitigate the increasing money laundering which has become the major challenge faced global by both banking sectors and financial services. The benefits of the Artificial Intelligence are that it can quickly analyse internal data before it can publicly publish. The desired data must be transactional across customer networks. The customer networks must include the Artificial Intelligence and cloud technologies as a learning machine comprising of data mining that can used either instantly or later. The AI must be a position to aid the analysis of the data to help make informed decision. The finance function should be able to perform risk assessment in due diligence and taking for example to enable lending quickly mostly by financial institutions. The challenge that must be carefully navigated to close the gap in the financial landscape that enables the AI backed by the appropriate cloud technologies to quickly learn and adapt to environments receiving inputs from various sources. The positive market sentiments held across most industries is that cloud computing maybe become vital towards Green IT.

The desired outcomes are value addition in the sense that repetitive operations performed by humans which are usually monotonous are eliminated. This culminates into reduced costs. In such circumstance’s accuracy levels are easily achieved and greater value is added to the business operations. A good client centricity of data creates sound data governance, data analytics and data management knowledge base. The challenge of the finance function is that there is low level of maturity, lack of investment in appropriate infrastructure. Players in the finance function has been reluctant to adopt to new AI and Cloud technologies available in the market due to prohibitive cost. In some quarters there is the challenge of increased complexity of latest technology resulting in reduced transparency in appraising the AI and cloud technologies in use by the finance team.

An estimated 97% of organisations globally are using cloud services in one form or another (McAfee 2018). Generally, cloud technologies provide a platform for the provision of various services including the financial service through the internet. The data in the form of data lakes or warehouses are saved remotely on a database (Slabeva and Wozniak 2010). On the other hand, Artificial Intelligence is the impersonation of intelligent human behaviour as embedded in machines (Dobrev 2012). These two technologies are widely used in the finance departments of organisations. A lot need to be learnt if they are to be studied together in the finance function not as in isolation like what it has been done in the past studies.

Data: The knowledge gap of research on the Artificial and Cloud Technologies in Finance Function or Team.

The technological development on its means that new concerns on Artificial Intelligence and Cloud Technologies in the organisation finance function, especially in the financial institutions. There is continuously gap in the literature to adequately address the new knowledge of combined AI and Cloud Technologies. The literature must be developed to cope with the changes. Some of the challenges faced by financial institutions that they are hesitant in adopting new AI and cloud computing technologies because of the risks associated with it since the financial industry is highly regulated requiring strict security and privacy measures (Bruce 2018). Although there is a huge number of benefits, many challenges arise to finance departments of organisations regarding AI and cloud computing to implement. There has been always a literature gap on the foremost challenge which occurs in the adoption of such technologies when it comes to cyber-attacks and security concerns prevalent in the industry.

Multi-tenancy is that another problem faced by the firms which means that their data including the financial data might be stored along with the data of their competitors which raises the questions of privacy concerns (Martel and Noleto 2019). This is a major challenge of data sharing to the finance function reduce the risk of fraud which may prove difficult to detect.

Analysis: The absence of literature theoretical frameworks on Artificial Intelligence and Cloud Technologies in Finance Function or Team.

Lack of any coherent literature for both Artificial Intelligence and Cloud Technologies in the finance function to keep pace with the advance of technology is a worrying challenge. According to an estimation, damages by cybercrime will cost approximately six trillion dollars by 2021 (Official Annual Cyber Crime Report 2019). Lack of clarity is the main reason such issues arise with questions regarding the access and management of financial information data processed by Artificial Intelligence and stored by cloud computing. Another challenge is that operating staff in many organisations lack skills to properly use AI and cloud technology in the finance function which delays their implementation and impact (Doyle 2019). Further finding an expert professional in this field is another issue since AI and cloud talent is a merger. Although the shift towards AI and cloud technologies is the need of the hour but the challenges in their implementation are creating hurdles for the finance function departments in their effective implementation.

Service Level Agreements

SLA can be defined as service level agreements. These are the type of agreements that are negotiated between a firm and a service provider. They define that a certain quality of service will be made available to the user for a certain period of time. A SLA provides details on the performance standards. However, it is essential to know that an SLA’s safety clause should not be compromising on the performance of the service for the client. It is essential that an SLA has been developed keeping in view that a detailed risk analysis is conducted. It is important that users remain aware of the provisions of the service level agreement and choose a certain SLA that does not compromise on performance which may affect the firm’s efficacy to address its needs (Elzamly et.al, 2019).

Dependence and Reliability

In addition to the above, the aspect of dependence and reliability also adds to the challenges of cloud computing for the function of finance. One of the most significant issues is that a company has excessive reliance on the provider. The reliability of the provider to provide smooth services through safe protocols also adds to this challenge. Moreover, considering switching from one provider to another is also more difficult as transferring information from one provider to another is not easy and may involve a lot of costs and challenges. Time could be another driving force. Moreover, browser dependence to access cloud systems, according to researchers is affecting the efficiency of the entire process (Akshaya and Purusothaman, 2016). Researchers are looking for better alternatives to ensure quick internet access for the users. Cloud computing efficiency is evaluated on the basis of four components; service reliability, software reliability, reliability of hardware and network reliability

The case study approach has been used to inform qualitative analysis. The case of Barclays Bank has been analysed with the collection of relevant data. Barclays Bank’s initiative to incorporate AI in its systems has been analysed. A case study approach has been used to identify key challenges facing implementation of AI for the finance function. The Barclays Bank has been used since the firm is one of the pioneers to promote the use of AI in financial services.

Barclays entry in the AI took place in 2012, when it launched its online banking app named, Pingit. The app soon began to have widespread applications as it could be used for transferring money from one banking app to another, social media banking, online shopping purchases and others. In addition, Barclays aims to increase investments in AI and provide its customers with a more personalized customer experience. Barclays understands that AI can be a potential source of its competitive advantage to be among the top five banks in the UK (Karanasiou and D. Pinotsis, 2017) It is concerned with introducing chat bots in its customer service department to manage queries of its customers. In addition to this, it is essential to know that the corporate culture at the company has always been supportive of deploying AI into business processes. Therefore, in order to counter challenges regarding the application of AI or cloud computing or any technology into the business processes, it is essential to know that the business has a culture that embraces change through technology (Datta, Tschanta and Dutta, 2015)

Besides, Barclays has developed an Eagle Lab, which is built with the aim to provide customers with useful insight of the AI technology and guide them through the use of AI-powered digital banking applications of the firm. Thus, the bank has the focus on replacing humans with machines for increased efficiency in operations. In addition, Barclays has also invested in voice recognition technology for telephonic conversations with the customer, thus enhancing customer security. Lastly, the bank is deploying AI systems to monitor fraud transactions (Bergen, 2016).

It is important to highlight that Barclays catalyst for AI implementation is two-fold; First, the firm aims to increase its revenues by bringing efficiency in operations. Data processing and validating may be a time-consuming task when manually performed. Hence, Barclays emphasizes on the need to increase efficiency in its operations and simplifying the service for its customers as much as possible. The second driver of AI initiative is the Bank’s response to competition. Competitor brands are benefiting from AI. This makes it essential for Barclays to equip itself with the necessary resources to respond to the stiff competition (Karanasiou &. Pinotsis,2017).

Although there are advantages of AI adoption, however, on the downside, the organizations considering to adapt Artificial intelligence may face certain ethical and legal challenges. It is important to highlight that these challenges apply to all financial institutes. First, increasing the use of AI powered applications and services on the digital will certainly impact branch operations, which, in turn will affect employment as jobs will be decreased. This adds to the ethical challenges of AI deployment. Moreover, it is also important to acknowledge that AI systems cannot be risked to operate for managing transactions involving large sums of money. Such transactions ought to be handled with human assistance. In addition, queries or requests concerning sensitive issues such as settlements of a deceased person may also require active human engagement in the process. Therefore, it is important to know that AI application is riskier for addressing sensitive matters and handling transactions of large sums of money (Datta, Tschanta & Dutta, 2015)

In addition to the above, there are many serious issues concerning customer privacy and the reliability of the AI technology as banks have a large amount of confidential customer data in their possession. Any misuse of such data or any compromise on security, which may be caused by biased algorithms of AI technology are likely to impede delivery of safe and efficient service to the clients. Therefore, gaining trust of customers regarding the technology is another big challenge for the business.

Conclusion and Recommendations

In conclusion, the case study approach has been used to inform qualitative analysis. The case of Barclays Bank has been analysed with the collection of relevant data. The Barclays Bank has been used since the firm is one of the pioneers to promote the use of AI in financial services. In order to counter challenges regarding the application of AI or cloud computing or any technology into the business processes, it is essential to know that the business has a culture that embraces change through technology. It is also important to know that AI application is riskier for addressing sensitive matters and handling transactions of large sums of money. In addition, gaining trust of customers regarding the technology is another big challenge for the business. Although adapting chat bots could help to boost customer request management processes, it is essential to know that these chat bots cannot operate voluntarily on their own. They need to be advised by humans to avoid bias in decision making.