i need a presentation on topic data mining and business analytics on banking sector. I have attached the research proposal from which you can get the information to make this presentation i have also attached file which tells the layout which needs to be followed
DATA MINING AND BUSINESS ANALYTICS IN BANKING SECTOR ITECH 5500 PROFESSIONAL RESEARCH AND COMMUNICATION Abstract The current proposal dwells on the topic of ‘Data Mining and Business Analytics in Banking Sector’ and gives an in-depth overview of the proposed research. In the proposal, it is found that data mining and business analytics are processes, which have gathered their importance from the modern development of Information Technology and increasing need of accurate business strategies. The proposal offers a brief description of the aim and the questions used as the core areas of consideration, which with the five chaptered structures gave a sense of academic research. The loss of relevance in case of data mining and the consideration of privacy breach in case of business analytics tend to emerge as the problems at hand, although the context remains the banking sector in general. Table of Contents 1.0 Introduction4 1.1 Overview4 1.2 Background of the Proposed Research4 1.3 Problem Statement5 1.4 Proposed Aim and Questions5 1.5 Significance of Proposed Research5 1.6 Structure of the Proposed Research6 2.0 Literature Review7 2.1 Data Mining7 2.2 Success Factors of Data Mining7 2.3 Limitations of Data Mining8 2.4 Business Analytics8 2.5 Success Factors of Business Analytics9 2.6 Limitations of Business Analytics9 2.7 Significance of Data in Banking Sector10 2.8 Impact of Data Mining on Banking Sector10 2.9 Impact of Business Analytics on Banking Sector11 2.10 Conceptual Framework12 3.0 Methodology13 3.1 Research Design13 3.2 Study Type13 3.3 Variables14 3.4 Data Collection and Analysis Method14 4.0 Discussion and Implication of Research15 4.1 Ethical Consideration15 4.2 Intended Outcomes15 5.0 References17 1.0 Introduction 1.1 Overview The wide popularity and global contribution in the globalisation process has made the economic statuses of greater masses and the financial transactions within and outside national boundaries, have made the banking sectors face greater challenges in terms of facing the competition, taking full advantage of the global growth opportunities and accurate marketing strategies for maximum profitability. Banks have also started taking the help of IT technology, in form of data mining and business analytics. These, on one hand, have given the scope of better assessment of the customers and clients’ market; while on the other hand, it has raised the question of data error assumptions and inference. In the proposed research, the use of data mining and business analytics in banking sector has been considered as the topic, offering better understanding of the sector and the nature of data mining and analysis functionality. 1.2 Background of the Proposed Research Similar to that of the globalisation, Information Technology (IT) has also evolved and has offered businesses their benefits through tools such as data mining and business analytics. With the business sectors using more of IT in their functional frame, an array of academic researchers has been conducted on IT and its incorporation in retail and education businesses as seen in the works of Ramageri and Desai (2013) and Bhullar and Kaur (2012). On the other hand, the banking sectors have also been the subject of academic discussion, as seen in the works of Kamel (2005) and Broadbent and Weill (1993). However, the incorporation of data mining and business analytics in the banking sector, which has recently become very prominent, has been discussed in inadequate proportions, keeping the assessment of the two variables, banking sector and data mining and business analytics understood on surface levels, lacking critical and in depth comprehensiveness. The proposed research takes a direct approach to explore the relationship between data mining as well as business analytics and banking sector, drawing relevance. 1.3 Problem Statement While data mining and data analysis aim to make the banking strategies accurate, the changes in recent trends make the data mining and business analytics assumptions irrelevant and improper for banks, raising question over the usage of the mentioned processes in banking sector. 1.4 Proposed Aim and Questions The proposed aims of the research are to assess the impact of data mining and business analytics in the banking sector. The proposed questions for the research are · What are the benefits of data mining and business analytics in the banking sector? · What is the significance of data dynamics in data mining and business analytics in banking sector? · What are the current issues of data mining and business analytics in the banking sector? · What are the ways to improve data mining and business analytics process in banking sector? 1.5 Significance of Proposed Research As the proposed research will be conducted, the dynamics of the data mining and business analytics applied in banking sector would be assessed better as the positive as well as negative aspects of the mentioned process will be mentioned, giving ample hint for banking organisations to develop better usage of the processes or reject them altogether, in both ways attaining higher profitability for their business perspectives. The proposed research, after its completion in a proper way would be able to provide hint at the potential banks have who have not used data mining and business analytics benefits, the risks existing banks have who are using data mining and business analytics in an improper way and the methods, through which the usage of data mining and business analytics would become more beneficial for the banking sector in the background of increasing market changes, trend transitioning and pressure of competition. 1.6 Structure of the Proposed Research The proposed research will have five chapters. Firstly, there will be the Introduction, which will give details under the subheadings of overview, background, rationale, problem statement as well as the aim, objective, questions and the significance of the research in context of the variables and their relationship. In the second chapter of the research, namely Literature Review, the research variables and the aspects, theories and factors will be discussed in a critical tone. In the methodology chapter, which is followed by Literature Review, would give details of the used methods for the proposed research, while in the fourth chapter, namely Findings and Analysis will collect data and analyse them based on the methods mentioned in the previous chapter. At last the conclusion will be drawn, based on the knowledge of all the previous sections of the proposed research. 2.0 Literature Review 2.1 Data Mining Data mining is the process of drawing accurate inference based on large pre-existing and accessible data processing on fixed and logic-based parameters. As stated by Amelio and Tagarelli (2018), data-mining processes involves the collection of long-term data, their process on computer-based processing software and development of a near future prediction of the trends, generally for strategic planning and risk assessment. On the other hand, Chaurasia, Pal, and Tiwari (2018), mentioned that data mining is a software-based simulation process, which helps in future potential assessment, majorly for commercial purposes. Data mining process uses huge amounts of data therefore, the help of computer software is taken, as the calculations are considerations of the multiple variables in the processed dataset requires proper understanding and accurate relationship identification. 2.2 Success Factors of Data Mining Firstly, data mining needs a lot of pre-existing data, as it can only perform and generate relevant information if it is given a lot of pre-existing data to be processes. As noted by Shapoval, Wang, Hara and Shioya (2018), the access to database and telecommunication advancement act as success factors of data mining, as the collected is needed to be collected from the clients’ database and should be transferred to processing data base of the software, which requires high speed and uninterrupted internet connectivity. As inferred by Chen et al. (2019), the accuracy of the processing software used for the data mining process is also a success factor, as the degree of accuracy and errors, as well as processing time determines the effectiveness and relevance of processed information generated through data mining. On the other hand, as stated by Wu and Lin (2018), the nature of the data and their quantities, processed through data mining are major decisive factors for generating effective and relevant data. As the collected data for data mining tends to have multiple variables in it, larger quantities are needed for accuracy purposes, although the higher quantities makes the information synthesis process delayed and the potential chances of error increases, although capable software are used, making the endeavour of creating useful information and investment total failures. 2.3 Limitations of Data Mining Although data mining is considered to a major IT tool for business benefit, there are some limitations of its usage. Firstly, as identified by Cano (2018), data mining cannot assure data privacy, as the data of users, customers and clients are given for processing to third parties, which makes the privacy aspect breached, often, raising concern with the entities whose data records are uses for data mining. In addition, the issue of data security is also provided in limited manners during data mining process. As mentioned by Chopra, Golab, Pretti and Toulis (2018), while data processing is being continued, the security of the data loses its security to a considerable degree and potential risk of unethical data hacking increases. 2.4 Business Analytics Unlike data mining, business analysis does not offer a general perspective at the time of considering and processing data. As opined by Power, Heavin, McDermott and Daly (2018), business analytics is an in-depth data processing technique, in which the data related to business and its statistics are considered important. Business analysis, as opined by Muller, Fay and vom Brocke (2018), is used for decision making, suggestive as well as the prediction of business purposes. In case of business analysis, the predictions and inferences are drawn on as data set both past and from current times are taken into a whole, unlike data mining, where the existing data tends to cater more significance than the current ones. As argued by Jalali and Park (2018), although having differences of perspectives between data mining and business analytics, it is found that both the variables influence one another in developing accurate assumptions. 2.5 Success Factors of Business Analytics Firstly, the availability of data for processing from the business organisation in focus is a major success factor of business analytics. As mentioned by Kremer (2018), the business analysis requires multi-dimensional data for processing and relationship comprehension, which can only be done, if there is data available that meet the criteria. Another success factor of business analytics is the clear idea about the respective goals of the chosen organisation, in the context of which the entire business analytics is being executed. As supported by Tursunbayeva, Di Lauro and Pagliari (2018), a clear idea about the organisational goals makes the ground of evaluation of different variables possible and the processed outcomes tend to offer higher degree of accuracy. As opined by Popovic, Hackney