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INFO802 Research In IT 2

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  • Course Code: INFO802
  • University: Waikato Institute Of Technology
  • Country: New Zealand

Question:

You are required to develop a preliminary research proposal that will draw on all aspects covered in the course. Emphasis will be placed on analysis and selection of an appropriate research method.

Your preliminary research proposal should include:

Introduction

In the introduction provide a summary of the context and content for your research proposal. You also need to provide an outline of the structure and sequence of the sections in your research proposal.

Problem statement

This is the most important element of your research proposal. The problem statement summarises what you research problem you are going to solve.

Research questions and/or hypothesis

If you are planning to use qualitative research you will need a main research question and sub questions. If you are planning to use quantative research you will

need a main research question and a list of hypotheses. If you are planning to use both qualitative and quantitative research you will need all of the above.

Literature review

This is a structured literature review of 20 peer-reviewed references that is organised according to the main themes for your research topic.

Methodology

The methodology section requires you to describe and justify your research design. You also need to describe and justify your chosen research method.

Contribution

In this section describe the contribution you believe that this research will make to your chosen topic.

Ethics approval

Outline all the ethics steps required for your chosen topic.

Data collection

In this section you will need to describe and justify how you plan to gather your data (survey, interview, experiment etc). You will also need to explain what your sample unit is, how you plan to sample, what population you are sampling and how your sample and sampling are appropriate for your chosen topic.

Evaluation metrics

In this section you will need to outloine how you plan to analyse the data you have gathered. The evaluation chosen will be dependent on the research method

chosen.

Timeline and costs

Outline how long each step listed above will take and any costs associated with wach step.

 

Answer:

Introduction

The paper mainly reflects on the impacts of data mining on financial sectors of Hamilton city. It is stated by Wamba et al. (2015), data mining is one of the procedures that generally extracts hidden, valid as well as actionable information from large databases for making a proper decision within the financial sector. It is identified that there is a number of areas in which the data mining can be utilized within the financial sectors of Hamilton city including credit analysis, prediction of payment default, fraudulent transactions, ranking investments, cash management and more.

 In this paper, a number of journal articles are generally reviewed in order to analyze the significance of data mining on the financial sector of Hamilton city. The paper also undertakes a mixed methodology method to collect information related to the research. Moreover, the paper also showcases that around 39 days are required with a budget of around $10,000 in order to finish the entire research successfully.

Problem statement

  It is found that the financial sector of the Hamilton city is highly competitive and therefore it is very much sensitive to both economical as well as political conditions.  As there are chances of a number of risks as well as challenges, it is very much important to use a proper key strategy that would be helpful in improving their performance by minimizing the costs as well as by increasing their revenues. In order to realize both the objectives, it is very much necessary to utilize data mining within the financial sector of the Hamilton city.

Research questions and hypothesis

The research questions are listed below:

  • What are the impacts of data mining on the financial sector of Hamilton city
  • Which part of the financial sector will feel the greatest impact due to data mining?

 Hypothesis 1:

H0: The use of data mining will be helpful in creating a positive impact on the financial sector of Hamilton City.

H1: The use of data mining will not be helpful in developing a positive impact on the financial sector of Hamilton city.

Hypothesis 2:

H0: Data mining is helpful in market analysis as well as customer insight

H1: Data mining is not helpful in  market analysis as well as customer insight

Hypothesis 3:

H0: Data mining is quite helpful in identifying the risk factors in each of the department of the banking business.

H1: Data mining is not helpful in identifying the risk factors in each of the department of the banking business.

 

Literature review

 According to Geng, Bose and Chen (2015), data mining is one of the process that helps in analyzing the hidden patterns of data as per which different perspectives for categorization into proper useful information which is generally collected as well as assembled in common areas including data mining algorithm, for efficient data analysis as well as in order to provide proper facility of decision making for cutting costs as well as increasing revenue (Tsai et al., 2014).

It is stated by Frizzo-Barker et al. (2016) that financial sectors generally follow two approaches in order to determine the fraud patterns as well as online as well as offline transaction check. For this reason, the various banking sectors purchases as well as maintains their data warehouse from compliance and anti-money laundering solutions as well as data providers.

  It is opined by Haghverdi et al. (2014), it is found that data mining generally plays a great role in the process of fraud detection from the various types of transaction data. It is quite necessary for the financial organizations to set proper standards as well as requests for producing various types of reports on a regular basis in order to reduce the chances of fraud (Wu et al., 2014).

Data mining process in the financial sector is a major trend.  It is stated by Kumar and Ravi (2016) that in banking sector, the process of data mining can help the banks to identify the customer’s borrowing and payment patterns.  It is helpful in identifying the services as well as products, which are favourable for its current, savings and credit customers.

  On the other hand, it is opined by Ahmed, Mahmood, and Islam (2016) that information has increased its importance in other parts of the financial sector tremendously. The investors and portfolio managers have made information gathering as the first and most essential part of the investment process. Therefore, they engage in gathering all available information about the stocks and other financial items in the portfolio so it is quite easy to determine the possible returns and the possible risks facing the portfolio before making the investment decision.

According to Geng, Bose and Chen (2015) that the available information has shown that the data mining process is very much important to the investment process and the banking process. It is found that the investor needs to understand the various characteristics of their bonds as well as stock.

It is stated by Wang et al. (2015), that data mining generally plays a great role in many organizations which is quite helpful in scrutinizing the data that is collected in order in order to deliver proper understandable pattern. It generally assists in dealing with the challenges that the banking industry faces.

Moreover, it is found that Hegazy, Madian and Ragaie (2016) data mining helps in facilitating proper useful data interpretations for getting better insights into the processes that are behind the data. In addition to this, it is found that data mining is quite helpful in targeting new customers for the services and products so that proper pattern of the customer purchase can be easily identified.

 It is opined by Keramati et al. (2015) that bank analyst generally analyzes the past trends for determining the present demand in order to forecast the behaviour of the customers for various products as well as services so that proper business opportunities can be grabbed quite easily. Additionally, data mining is quite helpful in identifying the customers that are profitable from the ones that are non-profitable.

 It is stated by Mansingh et al. (2015) that data mining is quite helpful in distinguishing the borrowers for repaying the loan promptly and additionally assists in predicting when the borrower is at default by providing loan to a specific customer. Data mining techniques are generally utilized by the bank executives for analyzing the reliability as well as behaviour of the customers while providing any service.

On the other hand, it is opined by Wamba et al. (2015) that most widely utilized area of data mining within the banking technology is mainly dependent on both consumers as well as commercial product marketing. The marketing departs of the financial organizations uses the algorithm of data mining for analyzing the present customers as well as for finding the products in which they are generally interested (Masud, Thuraisingham & Khan, 2016). 

It is stated by Chakravorty et al. (2015) that the utilization of data mining techniques is quite helpful in the strategic planning department for clustering the customers so that proper service can be provided to the customers. Moreover, it is identified that the techniques of data mining can be utilized for identifying the reaction of the customers on the adjustment in context to interest rates on the deposit as well as borrowing products.

 According to Hajian, Bonchi and Castillo (2016), it is found that data mining is useful in determining the various risk factors within the financial sector of the business. Approval authorities who are associated with the financial organizations generally use mining related data techniques for determining the various risk factors in lending appropriate decisions.

 On the other hand, it is stated by Gonzalez et al. (2015) that data mining is generally useful in three different phases of the customer relationship cycle including customer acquisition, increase the value of the customers as well as customer retention.

 According to Chen et al. (2015), It is found that the financial sectors generally hire the relationship managers for team executives for paying proper attention to the customers. Moreover, it is analyzed that the data mining techniques can be useful in offering better facilities to the customers of the organizations.

 

Methodology

Research design

According to Lewis (2015), research design is defined as one of the frameworks of methods as well as techniques that are mainly selected by a researcher for combining the various components of the research in a very much reasonable logical manner so that the entire research can be handled quite efficiently. It is found that in this particular research, descriptive research design is mainly selected so that proper in-depth information can be reflected on the significance of data mining within the financial sector of Hamilton city.

Research method

The research method is considered as one of the specific procedures as well as techniques that are generally utilized in order to identify as well as select the process in order to analyze the information related with a particular topic (Dang & Pheng, 2015).  It is found that both qualitative, as well as quantitative research methodology, is utilized within this project. The research undertakes qualitative research by reviewing various journal articles as well as papers however quantitative research methodology is used in order to conduct a survey in order to analyze the impact of the data mining on the financial sector of the Hamilton city (Dumay & Cai, 2015).

Research contribution

 It is found that by undertaking the entire research on the importance of data mining process on the financial sectors of the Hamilton city, it is analyzed that the utilization of data mining is quite advantageous as it is helpful in sorting large sets of data for identifying patterns as well as establishing problems with the help of data analysis. It is found that data mining helps in detecting fraud, assists in marketing as well as in managing risks which are associated with the different financial sectors.

Ethics approval

The research reviews online journals as well as articles and thus with the help of this type of research there is high chance that the financial sectors that are present within the Hamilton city need to expose some of the impacts that they have faced during the use of data mining. Therefore, in order to gather such type of data as well as information, it is very much necessary for the research group to take proper permission from the ethics committee so that the chances of the financial issue while conducting the research will not be present.

Data collection process

 It is found that mixed data collection method is generally utilized in order to gather data as well as information related with the research. It is found that qualitative data is collected by reviewing data about the significance of data mining within the research (Fletcher, 2017).  On the other hand, it is found that quantitative research is generally collected with the help of the survey that is conducted. It is found that in order to conduct the survey, the selected sample unit will be around 50 in size and the size of the population is 100. It is found that both sample size, as well as population size, is appropriate for conducting the survey as the response that is provided by the participants will be analyzed for ensuring the significance that is mainly associated with the research.

Evaluation metrics

It is found that a proper plan is developed in order to research the impact of data mining on the financial sector of Hamilton city. In order to collect data, mixed data collection method is mainly utilized. Both qualitative, as well as quantitative data, are gathered with the help of review as well as a survey. After the data are collected proper analysis is done on the data for making sure that the data that is gathered is helpful in reflecting that data mining is quite significant within the financial sector of the organization.

 

Project timeline and costs

 It is found that in order to undertake the research on the impact of data mining on financial sectors of Hamilton city, 39 days are needed with an amount of around $10,000. The schedule of the project is given below:

WBS

Task Name

Duration

Start

Finish

0

Impacts of data mining on financial sectors of Hamilton city

39 days

Fri 09-11-18

Wed 02-01-19

1

   Defining scope and problem

8 days

Fri 09-11-18

Tue 20-11-18

1.1

      Determining scope

2 days

Fri 09-11-18

Mon 12-11-18

1.2

      Determining aims and objectives

4 days

Tue 13-11-18

Fri 16-11-18

1.3

      Determining research questions

2 days

Mon 19-11-18

Tue 20-11-18

2

   Literature review

7 days

Wed 21-11-18

Thu 29-11-18

2.1

      Data analysis

2 days

Wed 21-11-18

Thu 22-11-18

2.2

      Analyzing the impact of data mining

3 days

Fri 23-11-18

Tue 27-11-18

2.3

      Analyzing the usefulness of data mining in the financial sectors

3 days

Fri 23-11-18

Tue 27-11-18

2.4

      Analyzing different case studies

3 days

Fri 23-11-18

Tue 27-11-18

2.5

      Reviewing data

2 days

Wed 28-11-18

Thu 29-11-18

3

   Research methodology

18 days

Fri 30-11-18

Tue 25-12-18

3.1

      Analyse the research methods

2 days

Fri 30-11-18

Mon 03-12-18

3.2

      Research design

4 days

Tue 04-12-18

Fri 07-12-18

3.3

      Data collection methods

3 days

Mon 10-12-18

Wed 12-12-18

3.4

      Perform the survey

5 days

Thu 13-12-18

Wed 19-12-18

3.5

      Data collection through questionnaire

2 days

Thu 13-12-18

Fri 14-12-18

3.6

      Analysis of data

4 days

Thu 20-12-18

Tue 25-12-18

3.7

      Development and implementation

3 days

Thu 20-12-18

Mon 24-12-18

3.8

      Validity and reliability of collected data

3 days

Mon 17-12-18

Wed 19-12-18

4

   Communication plan

5 days

Thu 20-12-18

Wed 26-12-18

4.1

      Identify the potential stakeholders

2 days

Thu 20-12-18

Fri 21-12-18

4.2

      Identify the communicational channels

3 days

Mon 24-12-18

Wed 26-12-18

5

   Closure plan

5 days

Thu 27-12-18

Wed 02-01-19

5.1

      Providing draft

2 days

Thu 27-12-18

Fri 28-12-18

5.2

      Finalizing draft

3 days

Mon 31-12-18

Wed 02-01-19

5.3

      Review the final project report

3 days

Mon 31-12-18

Wed 02-01-19

 

 

Conclusion

It can be concluded from the entire paper that the research that is undertaken reflects that data mining is quite significant for the financial sector of Hamilton city. It is found that with the help of data mining, the fraud that is associated with the financial sectors can be easily identified. Moreover, this technique also assists in managing risks as well as helpful in marketing in context to the financial sector. The data that are associated with the research are generally collected with the help of mixed data collection method. In addition to this, the paper also takes ethical issue approval from the ethics committee in order to avoid ethical issue within the entire research.

 

References

Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278-288.

Chakravorty, S., Daripa, S., Saha, U., Bose, S., Goswami, S., & Mitra, S. (2015). Data mining techniques for analyzing murder related structured and unstructured data. American Journal of Advanced Computing, 2(2), 47-54.

Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks, 11(8), 431047.

Dang, G., & Pheng, L. S. (2015). Research methodology. In Infrastructure Investments in Developing Economies (pp. 135-155). Springer, Singapore.

Dumay, J., & Cai, L. (2015). Using content analysis as a research methodology for investigating intellectual capital disclosure: a critique. Journal of Intellectual Capital, 16(1), 121-155.

Fletcher, A. J. (2017). Applying critical realism in qualitative research: methodology meets method. International Journal of Social Research Methodology, 20(2), 181-194.

Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), 403-413.

Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247.

Gonzalez, G. H., Tahsin, T., Goodale, B. C., Greene, A. C., & Greene, C. S. (2015). Recent advances and emerging applications in text and data mining for biomedical discovery. Briefings in bioinformatics, 17(1), 33-42.

Haghverdi, A., Öztürk, H. S., & Cornelis, W. M. (2014). Revisiting the pseudo continuous pedotransfer function concept: Impact of data quality and data mining method. Geoderma, 226, 31-38.

Hajian, S., Bonchi, F., & Castillo, C. (2016, August). Algorithmic bias: From discrimination discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 2125-2126). ACM.

Hegazy, M., Madian, A., & Ragaie, M. (2016). Enhanced Fraud Miner: Credit Card Fraud Detection using Clustering Data Mining Techniques. Egyptian Computer Science Journal (ISSN: 1110–2586), 40(03).

Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994-1012.

Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128-147.

Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), 473-475.

Mansingh, G., Rao, L., Osei-Bryson, K. M., & Mills, A. (2015). Profiling internet banking users: A knowledge discovery in data mining process model based approach. Information Systems Frontiers, 17(1), 193-215.

Masud, M., Thuraisingham, B., & Khan, L. (2016). Data mining tools for malware detection. Auerbach Publications.

Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T. (2014). Data mining for Internet of Things: A survey. IEEE Communications Surveys and Tutorials, 16(1), 77-97.

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246.

Wang, T., Rudin, C., Wagner, D., & Sevieri, R. (2015). Finding patterns with a rotten core: Data mining for crime series with cores. Big Data, 3(1), 3-21.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

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