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PUB HLTH 7300OL Developing A Research Proposal
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PUB HLTH 7300OL Developing A Research Proposal
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Course Code: PUB HLTH 7300OL
University: The University Of Adelaide
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Country: Australia
Question:
Write 5 goals and following the instruction given in the files and you have to make students profession as he doing job at Tim Horton coffe shop as counter staff so make information according and choose goals as well which should be related to it leave information regarding students details which is on first page you have to write as according to the assignment.
Answer:
Introduction
The paper mainly reflects on the “use of artificial intelligence in financial service”. It is found that earlier the banking organizations faces number of fraud in context to financial services and as a result they faces lot of difficulties in managing the operation of the organization properly (Cath et al., 2018). In addition to this, the banking organization also faces problems in managing finance as well as faces issues in providing proper financial advices to its clients. In order to resolve the risks and challenges that are associated with the baking industry, AI is used for managing the financial services (Spronck et al., 2018). It is identified that the use of AI not only detects fraud within the financial investment but also assists in providing proper help in context to financial advisory services as well as in managing the finance of the organization. The research questions on which investigation will be undertaken is provided below:
How AI is useful in managing the financial services?
What are the benefits that are achieved by using AI in managing the financial services?
Does the utilization of AI in context to the financial services create any type of risks or challenges?
It is found that numbers of journals as well as articles are generally reviewed in order to analyze the significance of AI in managing the financial services. In addition to this, proper research methodology is also provided in the paper in order to discuss the research methods, as well as data collection methods that are mainly used in order to undertake this entire research.
According to Li et al.(2017), most of the organizations that are mainly related with the financial services are generally attempting for leveraging AI so that powerful as well as connected journey of the customers can be created. The baking industry are using AI based face recognition system in which the customers face is recognised and after the identification of the person, proper details that are mainly related with the customers are revealed (Kokina & Davenport, 2017). Thus, it is found that AI assists the banks in discovering the activities of the customers and as per the actions of the customers proper strategy is mainly suggested to the customer, which can be considered beneficial for the organizations.
Wong, Ho and Tsui (2017) stated that several banking industry are generally utilizing AI for driving the customers and for helping them to automate their personalized interactions. With the utilization of AI, the operation of the banking industry is improved and the use of AI further helps in managing the cost of the organization. It is found that the utilization of RPA technology is considered as one of the enabler in context of brining efficiency as well as profitability within the entire organization (Zi?ba et al., 2016).
Shen and Tzeng (2015) opined that monetary administrations suppliers are immersed with enormous information, particularly unstructured information. It is fund that AI-based frameworks is considered to be conceivable an in helps in breaking down volumes of business information and assists in discovering how well the interior control frameworks are working (Kshirsagar et al. 2016). Progressively, budgetary associations are receiving a machine learning-based way to deal with increase their algorithmic tenets based methodology towards fraud detection system. Moreover, the learning strategies, which are always learning at work, can keep a couple of ventures in front of human-and guidelines based misrepresentation identification frameworks. AI and machine learning are ended up being distinct advantages in identifying insider trading to market abuse.
According to Tirgul and Naik (2016), it is found that most of the players within the insurance sector plays a great role in resolving the complex challenges and issues in various areas including fraud detection, underwriting, claim handling and more. It is found that the insurers generally look for the data that generally assists in understanding the customer’s behaviour in a very much effective way. Anything which are very much helpful in allowing them to charge for the risks and to offer less prices to others so that he price on an average can be reduced (Moro, Cortez& Rita, 2015). Furthermore, it is found that the use of artificial intelligence plays an important role in insurance underwriting and claims. By analyzing the given fraud incidents, it is very much necessary for the financial players to provide careful consideration to the security and protection of client information. At no other time had the business felt such a squeezing requirement for viable compliance and governance (Brynjolfsson, Rock& Syverson, 2017). Moreover, it is found that due to the increase in the use of AI, it is considered very much helpful for developing number of creativities within the financial industry that further assists in providing number of opportunities to the customers of the organizations.
Benefits of using AI in managing the financial services
The benefits of using AI in managing the financial services are mainly elaborated below.
AI helps in fraud detection as well as management: According to Shynkevich et al. (2015), it is found that every business organizations generally aim to minimize the risk condition from the organization. It is found that AI plays a significant role in resolving the problem that baking industry mainly faces due to the fraud detection. It is found that AI can use past behaviours on various transactions related instruments for pointing out the old behaviour. In addition to this, it is found that the use of AI generally does not have any qualms. Thus, it helps in raising a red flag for the procedure of transaction the human. It is identified that the use of AI not only detects fraud within the financial investment but also assists in providing proper help in context to financial advisory services as well as in managing the finance of the organization.
Providing financial advisory services: According to Zhou, Goh and Shen (2016), it is found that as indicated by the Pwc Report, one can anticipate more robo-advisors. With the pressure increment on budgetary establishments to diminish the rates of bonus on singular ventures, machines may do what people don’t-work for a solitary initial instalment. Another advancing field is bionic warning, which joins machine counts and human knowledge to give choices that are significantly more proficient than what their individual parts provide (Chiang et al., 2016). Collaboration is critical. It is not sufficient to take a gander at a machine as an adornment, or on the opposite end, as an agonizing know-it-all. Moreover, it is found that proper balance as well as ability is generally required in context to human viewpoint in order to make proper decisions.
Managing finance: Managing finance is considered as one of the most challenging tasks. It is found that as AI assists in managing the finance and therefore, RFM is considered as one off the most recent developments that are generally dependent on the AI based wallet. It is analyzed that AI is mainly utilized in order to develop algorithm so that the consumers can be able to make proper decisions about their money dung the time they generally engages in spending money (Cath et al., 2018). The idea that is associated behind the AI based wallet is found to be quite simple as it mainly engages in accumulating information from the web footprint and then proper spending graph is generally created.
Trading: According to Spronck et al. (2018), investment organizations have been depending on PCs and information researchers to decide future examples in the market. As an area, exchanging and ventures rely upon the capacity to foresee the future precisely. Machines are incredible at this since they can crunch an immense measure of information in a brief time. Machines can be instructed to watch designs in past information and anticipate how these examples may rehash later on. While peculiarities, for example, the 2008 money related emergency do exist in information, a machine can be educated to think about the information to discover ‘triggers’ for these oddities (Kokina& Davenport, 2017). Therefore, it is necessary to depend on AI for choices on when to purchase, hold and offer stock. One with a lower chance craving can get cautions for when the market is required to fall, and would thus be able to settle on a choice about whether to remain put resources into the market or to move out.
Challenges or limitations of AI in managing the financial services
It is found that there are number of challenges and issues that are mainly related with the utilization of AI in managing the financial services. It is found that the services as well as challenges that are mainly associated with AI in managing the financial services are mainly elaborated below:
Customer privacy: It is stated by Shen and Tzeng (2015) that the use of AI within the baking industries can create umber of security as well as privacy issues as the confidential data hat are present within the organization can get leaked due to the utilization of AI. It is found that customer privacy is one of the significant factor before any organization and thus it is very much necessary as well as important to secure the data as well as information off the customers very much effectively so that it does not get leaked.
Technological complexity: It is found that it is very much difficult to utilize AI within the baking sectors. It is identified that the utilization of AI within the baking industries generally creates number of technological complexities within the organization, which need to be resolved quite effectively (Tirgul& Naik, 2016). Thus, managing the technological complexity while utilizing AI for managing the financial services within the organization is considered to b advantageous.
Loss of job: According to Brynjolfsson, Rock and Syverson (2017), is found that due to the use of AI within the baking sectors, it is found that the entire operations as well as functions of the banking industry will be managed automatically.Thus it is found that by making the functions as well as operations of the organizations automatic, the people who are engaged with that type of work will generally loose job. This is considered as one of the major issue in using AI for managing the financial services of the organizations.
Security of AI systems:It is found that the banking industries faces lot of challenges as well as difficulties in providing proper security to the AI systems and this is mainly due to the autonomous weapons, which generally can cause of damages within the baking sector. Thus, in order to remain secure it is quite necessary to provide proper security services so that it does not face any type of issues or challenges (Chiang et al., 2016). Thus, it is analyzed that proper security of AI systems are very much necessary for managing the operations of the organizations.
It is found that there are mainly three types of research philosophies including interpretivism, positivism as well as realism. It is found that in this specific research study, the researchers uses the positivism philosophy while undertaking the research as it is very much helpful in understanding the reality of the entire research work which will generally be conducted (Lewis, 2015). It is found that it will generally undertake the entire research study in order to do number of analysis on the data collection method for undertaking appropriate framework. Thus, the research philosophy that is generally required for understanding the entire topic of the research quite effectively.
It is found that depending on the methodology, it is found that the research approach is of two types such as inductive method as well as deductive method. It is identified that in this specific research study, deductive research approach is mainly used (Smith, 2015). It is found that the main reason of using the deductive approach is that, this method generally depends on the theories that are established as well as on the da collection methods that are used for fulfilling the gap that generally presents within the entire research study. It is found that in this specific case, inductive research approach method is not used for one of the cases however it is found that this approach assists in reflecting the entire research framework, which is generally undertaken by the researchers (Choy, 2014). It is found that the research questions, which are generally, discussed above assists in learning much more dominant concepts, which are mainly, related it he enterprise modelling including Doman model.
It is found that the data collection methods are generally of two types such as secondary method of data collection ad primary method of data collection. It is found that in context to primary data collection method, the researcher needs to take the help off survey, interview as well as questionnaire in order to collect data and for undertaking the research properly (Dang and Pheng, 2015). However, it is found that in order to collect secondary data, the researcher needs to research on the provided topic and by reviewing number of journal articles as well as papers, proper information related with the topic is found so that the entire research can be undertaken successfully.
It is found that in specific research, secondary data analysis method is mainly used in order to collect proper information as well as data about the utilization of artificial intelligence for managing the financial services within the making industries. It is found that in order to undertake the research, number of journal articles as well as other secondary sources of data collection method is mainly used in order to analyze as well as collect data and information that are considered significant. It is analyzed that by using proper information from the articles, the entire research is conduced successfully.
Research design is defined as one of the blueprint of the entire research work that is conduced as it generally assists in discussing the research plan in detail for analyzing the collected data as well as information very much effectively. It is found that there are three main type of research design, which generally includes descriptive, exploratory as well as explanatory (Fletcher, 2017). It is identified that in this specific research study, explanatory research design I mainly used in order to reflect the utilization of financial services for managing the financial services of the organizations. It is found that this research design is mainly used wile undertaking this research as the researcher can be able to properly understand the nature off the problem that is associated with the research including the purpose as well as knowledge of the researchers. It is analyzed by using this specific type of research design, the gap, which are generally associated with the research, are generally identified. It is found that successful identification of project limitation, it is the responsibility of the researchers to undertake proper possible sets so that the research design methods are improved and thus it becomes much more helpful in meeting the objective and goals of the organizations.
It is found that the entire research was mainly conducted s per the research guideline in order to maintain the ethical considerations. The entire research was submitted on time by involving within the research as per the knowledge as well as skills of the researcher (Walliman, 2017). It is found that the data that is associated with the research is not disclosed with the help of the third party and thus the confidentiality as well as privacy of the data is maintained. Moreover, it is found that the researcher selections all the questions of the research as per the topic and thus the entire research are completed as per the rules of honesty by following the ethical considerations.
It is found that the specific research that was mainly undertaken on the paper reflects hat AI is very much helpful as well as advantageous in managing the financial services of the organizations. It is found that the use of AI does not assists in managing the financial services but it also helps in trading, detecting fraud as well as in managing the risks that are associated with the different banking operations. In spite of the advantages, it is identified that there are number of limitations of AI utilization, which needs to be considered while managing the financial services.
Timeline and Gantt chart
The timeline or the Gantt chart for the undertaken research on the utilization of AI for managing the financial services is provided in the table that is given below:
Activities undertaken in this research
First Week
Second Week
Third to Fifth week
Sixth to Eighth Week
Ninth to eleventh Week
Twelfth Week
Selection and approval of the topic
ü
Determining the research questions
ü
Undertaking literature review
ü
Selection of proper research methods
ü
Data collection through secondary data analysis
ü
Analyzing outcome
ü
Findings as well as review on the research study
ü
Determining the limitation of the proposal
ü
Conclusion Recommendation
ü
Final report submission
ü
Conclusion
It can be concluded from the research that the use of artificial intelligence is quite beneficial for managing the financial services. It is found that AI not only assists in managing the financial services within the banking sector but also helps in managing the risks, helps in detecting fraud, assists in trading and more. The paper reflects number of benefits of AI within the baking industry.It is found that due to the increase in the use of AI, it is considered very much helpful for developing number of creativities within the financial industry that further assists in providing number of opportunities to the customers of the organizations. Furthermore it is found that the paper utilizes secondary data collection method in order to gather information about the research. Moreover, the research paper also illustrates the limitations of AI within the baking industry. In addition to this, it is analyzed that the entire research that was undertaken was completed as per the ethical guidelines.
References
Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In Economics of Artificial Intelligence. University of Chicago Press.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach. Science and engineering ethics, 24(2), 505-528.
Chiang, W. C., Enke, D., Wu, T., & Wang, R. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195-207.
Choy, L. T. (2014). The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19(4), 99-104.
Dang, G., & Pheng, L. S. (2015). Research methodology. In Infrastructure Investments in Developing Economies (pp. 135-155). Springer, Singapore.
Fletcher, A. J. (2017). Applying critical realism in qualitative research: methodology meets method. International Journal of Social Research Methodology, 20(2), 181-194.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115-122.
Kshirsagar, G., Chandel, M., Kakade, S., & Amaria, R. (2016). Stock market prediction using artificial neural networks. International Journal of Advanced Research in Computer Engineering and Technology, 5(5), 1691-1695.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), 473-475.
Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96.
Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
Shen, K.Y. &Tzeng, G.H. (2015). A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft Computing, 19(4), pp.859-874.
Shynkevich, Y., McGinnity, T. M., Coleman, S., & Belatreche, A. (2015, July). Stock price prediction based on stock-specific and sub-industry-specific news articles. In Neural networks (ijcnn), 2015 international joint conference on (pp. 1-8). IEEE.
Smith, J. A. (Ed.). (2015). Qualitative psychology: A practical guide to research methods. Sage.
Spronck, P., André, E., Cook, M., & Preuß, M. (2018). Artificial and Computational Intelligence in Games: AI-Driven Game Design (Dagstuhl Seminar 17471). In Dagstuhl Reports (Vol. 7, No. 11). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
Tirgul, C. S., & Naik, M. R. (2016). Artificial intelligence and robotics. International Journal of Advanced Research in Computer Engineering & Technology, 5(6), 1787-1793.
Walliman, N., 2017. Research methods: The basics. Routledge.
Wong, B., Ho, G. T., & Tsui, E. (2017). Development of an intelligent e-healthcare system for the domestic care industry. Industrial Management & Data Systems, 117(7), 1426-1445.
Zhou, Z., Goh, Y. M., & Shen, L. (2016). Overview and analysis of ontology studies supporting development of the construction industry. Journal of Computing in Civil Engineering, 30(6), 04016026.
Zi?ba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93-101.
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