Draft outline 2,000words. You can decide to write the topic from the files that I send to you. when you finish your decision could you please tell me. And if you can any questions please email me. Thank you . Because it is really important to me.
RESEARCH Proposal (GUIDELINE)
- Research topic
- This should projects a clear topic/area of the research proposed.
- Research background / rationale
- Suggestions on the national and international research status of this topic, the significance of this topic (a gap that can be filled).
- Literature review
- Based on the research topic, suggest the types of literature review should be reviewing according to the key concepts of the topic.
- Recommendations on which direction the literature should be focused on.
- Aim & Objectives
- Clearly outline the research aim
- Research objectives link with the research aim (e.g. To investigate…, To evaluate…, To assess…, To verify… etc.)
- Research questions
- At least one major research questions with a couple of associated sub-questions (with ‘?’ at the end)
- Research hypotheses
- Based on the research questions, two or three hypotheses should be attempted to answer the questions above.
- At least one or two methods suggested to be employed to address the questions mentioned above, and these methods should be able to solve the questions raised above.
- The relevance of the suggested methods.
- Describe the relevant data sources and justify the use of them. Propose a sample period based on data availability.
- Potential contributions for carrying out the research
- Reference / Suggested reading materials
- Suggestions on the core articles the research should be focused on
- There is a one-to-one correspondence between the citations and the reference. Every citation should be in the reference section, and every reference should be cited.
- Please focus on the top journals.
|Subject||Writing a proposal||Pages||13||Style||APA|
This section presents a brief background information to the topic of the research. It is organised into six sections which include background information, statement of the problem, the objectives of the research, the research h question, justification of the study and finally the scope of the study
The drastic evolution of information technology has brought with it advantages of making work easy and sometimes more efficient. Artificial Intelligence (AI) is a branch of Information Technology (IT) involving computerization of information, and the scientific manipulation of the same information to produce a system than can almost autonomously perform some task accurately and precisely. Most organization have adopted AI in their line of production for example Toyota using robots in the manufactures of motor vehicles (CITE THE BLANK NRACKET). In many other fields such as medicine, AI is used to conduct surgical operation. While some AI are produced in physical form such as robots, some are computerized softwares such as, SPSS, ANOVA, STATA, Quickbooks, FreshBooks, Enterprise Resource Planning (ERP), Crunched, Sage Intact, Xero, and Commercial Accounting Software. This software has significantly made work easy (FinancesOnline, 2018, 2). According to Acemoglu, D. & Restrepo, P. (2017, 57), the softwares have improved operation efficiency, reduced opex, automated record keeping, improved accuracy and secured data. Currently the rate of adoption of AI in Finance and accounting is low but globally but significantly growing in US. For example, JPMorgan Chase have adopted a Contract Intelligence (COiN) (the way this has been defined should apply to all initial at first mention. Subsequent mentions you can just use the initials)system which they use to analyse legal documents and extract important data points and clauses, Well Fargo uses AI Enterprise Solution, and also Startup Accelerator program. Other Banks like Bank of America, CitiBank, U.S. Bank, Bank of NY Mellon Corp. and PNC. There is however the concern of “augmenting” and replacement of employees which is the foundation of the dilemma (Kumba, S. 2018, 3).
Literature and empirical evidence both testify of the benefits of adoption of AI in business and most particularly the finance industry. BNY Mellom Corp for instance reports 100% accuracy in the account – closure validation across five of its systems, 66% improvement in trade entry turnaround time, 88% improvement in processing time and ¼ second robotic reconciliation of failed trade vs. 5-10 minutes by humans (Kumba, S. 2018, 13). These benefits notwithstanding, the equation to balance or rather to solve is the concern that AI is replacing human employees and the need to strike a moral, economical and socially acceptable balance. It is a is not just s present time dilemma but a more magnifies future dilemma.
The objective of this research is to find out how well adoption of inevitable AI can be balanced with maintenance of acceptable, and sustainable human employees while at the same time maintaining moral integrity and economic viability.
- What are the common trends in efforts by Financial institution in adopting AI?
- How can the inevitable AI be adopted in a morally, economically and more socially sustainable way in industries dominated with human intensive operation?
Finding a solution to the dilemma will be a broad step not just for human resource managers but also for the general management of financial institution. This is because all of them desire to benefit from the beauty of AI but the theory of Utilitarianism is equally a factor to not just consider but implement.
The scope of application of AI is wide and the dilemmas associated with the application are equally diverse but this research focuses on the application of IA in USA based Financial Institutions and the dilemma it poses to human resource managers with regards to the question of
This section briefly focuses on some of the published literature that focus on the subject of adoption of AI and the past, the present and future of AI adoption as well as the associated impacts particularly on HR.
According to Kumba (2018, 2), the emergence of AI was not accidental because it has found proper usage with even further demand for more refined AI. The application of AI has been adopted in many industries including health, manufacturing, entertainment, and the banking industries. In the Banking industry, AI has been used in domains such as Cost predictions, automation of data backup, check printing, budget forecasting, fund accounting, inventory management, payroll management, customization of reports, external application integration and other areas (FinancesOnline 2018, 2). A case study of US top seven banks gives a brief feedback on some of the AI they have adopted, and how they have benefited. The report also captures some of the dilemmas associated with the adoption of AI. At JPMorgan Chase for example, a total of $9.5 billion was invested in the technology in 2016 alongside an allocation of $600 million for emerging fintech solution (Kumba, 2018, 6). AI has therefore become a competition tool that many other organizations consider it as a timely solution for some of the common mistakes such as human error. Although the technology is applauded for the tremendous impacts is has had on the seven top banks in the US and other banks across the globe, it is linked to serious economic/job loss concerns alongside other concerns such as the cost and significant difficulty of acquiring and maintaining machine learning and AI talent (Dauth et al., 2017, 162).
According to Daniel F. (2017, 2) an interview with the CEO, chair and co-founder of Rebellion Research (Alexander Fleiss), revealed that adoption of AI in any organization translates to job loss and thus disrupts the economy. Mr. Fleiss noted some of the employees who are directly pushed out of employment by adoption of AI. These employees include brokers, financial advisors, banker/tellers, bank office workers and any others whose work involves paperwork. This is because these activities can accurately, and more efficiently be performed through computerized systems and software and interestingly, one single computer system can be instructed to perform various duties which would otherwise require specifics expertise of different experiences. In simple terms, one single software can replace many employees with just one expert running and managing it or even none at all. The organization are also in the dilemma of minimizing cost of production. The rationale is that there may be no need to retain and sustain a large pool of employees when their work can better be performed by AI (Wilson, J., Daugherty, P.& Morini, N. (2017, 32)
It is evident that AI can replace many employees and thus reduce the cost of production. It however implies a disrupted economy. There is therefore need to establish how well a balance can be struck to handle to two realities.
The chapter establishes the method to be employed in conducting the research. It thus defines the research materials, research design, data collection method and how the data would be analysed, discussed and conclusion drawn from it.
The main rese4arch material in websites and authentic data bases
The research is solely a meta-analysis of the findings of previous related studies of the same subject. The research studies to be reviewed are those retrieved from credible and authentic data bases so that the study is as reliable as possible
The Data has been retrieved from certain data bases and websites as cited for every data included in the study.
Investments on AI have the potential of boosting the bank revenue by about 34% by 2022 (Daniel, F. 2017, 1).at the same time, AI is considered to one of the most critical disruptive technologies according to a survey conducted by PwC. PwC established that 72% of senior management consider AI and machine learning (ML) as a competitive advantage. The same survey also found that 52% of financial companies are already making significant commitments to AI while 66% of the same institutions are projecting tangible investments by 2020. Figure 1 below show the dynamics between AI (Robots in industries/1000 employees) adopted in China, EU and US (Data from International Labor Organization (2017, 42), IFR (2016, 97).
Figure 1: Figure 7.1 Robot density in China, EU and US.
Source: Data from International Labor Organization (2017), IFR (2016).
According to Dauth et al. (2017) every robot adopted causes around two manufacturing jobs loss. This translates to about 275,000 loss of manufacturing jobs between 1994 and 2014, which accounts for thereabouts 23% of falloff between the two periods. Dauth and his colleagues emphasize that the loss was more than offset by employment growth in the services industry. There is expansive accord in various studies that lots of the on hand jobs (about 50% in the US and UK) are most likely to be automated. Nonetheless, there is no agreement on the exact fraction of jobs could entirely be automated or totally transformed. The projected share of current jobs that could technically be automated in the future fluctuates between 9% (AGZ) and 47% (FO) in the US, and between 10% (AGZ) and 30% (PWC) in the UK (note that FO provide estimates for the US only) (Frontier Economics, 2018, 41). Figure 2 below shows the approximated percentage of jobs at high automation likelihood in the US.
Figure 2: Estimated Fraction of Employment at High Automation Probability
Source: Arntz, M., Gregory, T. & Ziehran, U. (2017, 153)
This chapter presents interpretation of the data presented in the immediate previous section, discussion of the same and the implication of the same to the objectives of the research.
From the pieces of data presented, it is very clear that the adoption of AI is to the advantage of the employer but at the same time, it poses a threat not just to the employees at risk of losing their jobs, but the general economy supported by the same employees. The simple rationale is that AI can be programmed to perform a range of jobs within a very short times. One single computer can be programmed so that is can do auditing, accounting, facilitation of transitions, record keeping and many other related jobs (Frey, C. & Osborne, M. 2017, 258). The implication for an employer is that there is no need of keeping for example ten employees when one single AI can perform their roles more efficiently, accurately and even faster. Considering JPMorgan Chase using Contract Intelligence (COiN), manual reviews of 12,000 annual commercial credit agreements under normal manual labour requires about 360,000 hours which is equivalent to 41.09 years. The same work can be done but COiN AI in a matter of seconds (Kumba, S. 2018, 2).
This probably explains why the trend in Figure 1 is witnessed with drastic changes in in the past two decades. If for instance, the scenario of the US is considered (9-47% job loss to AI), the implication would be detrimental to the working population. The present employment rate in the US is 60.60. The employment is distributed as shown in Figure 3. The current employment is at 155.07 million people (Statista 2018, 1).
Figure 3: Employment in the United States from 2009 to 2019 (in millions)
Source: Statista (2018)
Finance industry employs about 10.482 million people (Statista, 2018, 1). This represents about 6.76% of the total employments. Now if for instance this number goes down by the minimum projected job loss to AI (9%), then it would mean 0.94 million people will be rendered jobless in the banking industry alone. If the projected 47% job loss to AI adoption is experienced, then 4.93 million people will be rendered jobless in the finance industry alone. The situation presents a dilemma because the economy faces a tragic slope in the event that AI is adopted without a second thought of the fate of the employees (Gordon, R. 2016, 4).
The question is not what HRs are to do, neither is a question of what are the “endangered species” (employee whose jobs are at risk of replacement by AI) to do. It is a question of how the economy can be sustained both at micro and macro levels. A further research establishes some of the proposal of handling the dilemma. First, there is need to assess tasks and skills. This would help in evaluating the need of some employees regardless of adoption of AI. Secondly, creation of new but unique role. It may look complicated but it should be understood that advancement of AI enables employees to take on higher value work. Consequently, employees need new roles driven by insight and strategy rather than mono-skilled and monopolistic jobs. Finally, there is need of mapping skills to new roles (Susskind, R., & Susskind, D. 2015, 82).
AI adoption is an idea whose time has come timely and no organization will close their eyes to it. In fact, nearly all organization across the globe have adopted a form of AI. All banks have embraced mobile banking, and they have ATMs. These are forms of AIs and therefore the question of whether AI will be adopted or not does not hold water. Again the reality of job replacements is very real and may be inevitable at some time. With the improved efficiency, accuracy and reduced cost of production associated with AI, it is a critical competitive advantage. There is however an equally critical need to strike balance, not for the sake of the employees alone but also for HRs and most importantly for the general economy. The Utilitarianism must apply.
Kumba Sennaar (2018). AI in Banking – An Analysis of America’s 7 Top Banks. techemergence. Retrieved on 22nd November, 2018 from: https://www.techemergence.com/ai-in-banking-analysis/
Daniel Faggella (2017). Rebellion Research’s Alexander Fleiss – How AI is Eating Finance. Techemergence. Last updated on December 3, 2017. Retrieved from: https://www.techemergence.com/rebellion-researchs-alexander-fleiss-how-ai-is-eating-finance/ on 11th November, 2018
Dauth, W., S. Findeisen, J. Südekum and N. Woessner (2017), German Robots: the Impact of Industrial Robots on Workers, CEPR Discussion Paper DP12306.
Acemoglu, D. and P. Restrepo (2017), Robots and Jobs: Evidence from US Labor Markets, NBER Working Paper 23285, http://www.nber.org/ papers/w23285.pdf.
Arntz, M., T. Gregory and U. Zierahn (2016), The Risk of Automation for Jobs in OECD Countries: a Comparative Analysis, Social, Employment and Migration Working Paper 189, Paris: Organisation for Economic Co-operation and Development.
Frey, C. B. and M. A. Osborne (2017), ‘The Future of Employment: How Susceptible Are Jobs To Computerisation?’, Technological Forecasting and Social Change, 114: 254–80
Statista (2018). Employment in the United States from 2009 to 2019 (in million). The Statistics Portal. Retrieved on 23rd November, 2018 from: https://www.statista.com/statistics/269959/employment-in-the-united-states/
Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford: Oxford University Press
Gordon, R.J. (2016). Perspectives on The Rise and Fall of American Economic Growth. American Economic Review: Papers & Proceedings 106(5), 1-7.
FinancesOnline (2018), Benefits of Accounting Software: Examples of Leading Solutions Explained. FinancesOnline. Last updated 11th Sep. Available at : https://financesonline.com/benefits-accounting-software-examples-leading-solutions-explained/. Accessed on 23rd November, 2018.
Arntz, M., Gregory, T. & Ziehran, U. (2017) The Risk of Automation for Jobs in OECD Countries (OECD Social, Employment and Migration Working Papers No. 189). Available at: https://www.keepeek.com//Digital-AssetManagement/oecd/social-issues-migration-health/the-risk-of-automation-for-jobsin-oecd-countries_5jlz9h56dvq7-en#page1. Accessed on 23rd November 2018
Frontier Economics (2018). The Impact of Artificial Intelligence on Wor: An evidence review prepared for the Royal Society and the British Academy. Frientier Economics. Available at file:///C:/Users/HPUser/Desktop/Chief%20Ycliffe/Personals/frontier-review-the-impact-of-AI-on-work.pdf. Accessed November 23, 2018