Case Study on Data Mining in the Finance Industry
Added on 2020-04-15
11 Pages2479 Words43 Views
Running head: DATA MINING IN THE FINANCE INDUSTRYData Mining in the Finance IndustryName of the StudentName of the UniversityAuthor’s note
1DATA MINING IN THE FINANCE INDUSTRYTable of Contents1. Introduction.................................................................................................................................22. Discussion....................................................................................................................................22.1 Background of Data Mining......................................................................................................22.2Different Methods and Techniques covered by Data Mining....................................................42.3 Data Mining Applications in the Financial Sector....................................................................62.4 Different Data Mining Applications and Methods Used -Case Studies....................................62.5 Challenges of Data Mining in Financial Industry and Future Development.............................73. Conclusion...................................................................................................................................84. References..................................................................................................................................10
2DATA MINING IN THE FINANCE INDUSTRY1. Introduction Data mining is carried out for the purpose of discovering valuable patterns andinformation from data warehouses or databases (Larose 2014). Financial data plays a significantrole in the financial sector for analyzing consumer data in order to find legitimate customers suchas the debtors. Data mining has become an integral part of the financial sector as it helps inpredicting the future behaviour and trends in the financial markets.This report gives an overview of the concept of data mining. It discusses the varioustypes of methods or operations of data mining in the financial industry. It also examines andanalyzes some of the challenges of data mining in the financial sector. This report gives a briefoverview of the future developments that can be done in the banking sectors, investment sectorsand other financial institutes by applying data mining techniques and methods.2. Discussion2.1 Background of Data MiningData mining is known to be a process that extracts hidden knowledge from largedatabases that contain raw data (Witten et al. 2016). It can also be defined as the science ofextracting valuable information or discovering knowledge from data warehouses (Larose 2014).Knowledge is discovered in the data mining process. There are various steps in data miningprocess that follows an iterative sequence. The steps of data mining process are:Name of the stepAnalysis 1. LearningThis step focuses on learning and getting priorknowledge about the application domain. The goals andobjectives of the application need to be learnt beforecarrying out the process of knowledge or information
3DATA MINING IN THE FINANCE INDUSTRYdiscovery.2. Creating of target datasetThis step focuses on creating and identifying a targetdataset. Selection of the dataset must be done correctlyin order to apply data mining processes and methods fordiscovering knowledge.3. Data cleaning This is a pre-processing method that carries out basicoperations like removal of noise. This stage focuses onremoving inconsistent data and errors (Preethi andVijayalakshmi 2017).4. Data projectionData projection is carried out in this stage. This stepfocuses on discovering valuable features for the purposeof data representation.5. Data mining functionThis step focuses on taking a decision regarding theobjective of the data mining model derived. Datamining function is selected in this step.6. Data mining algorithmThis step focuses on selecting a method for searchingfor data patterns.7. Data patternsThis step includes classification trees or rules,clustering, regression, line analysis and sequencemodeling for finding out data patterns.8. Pattern interpretation The discovered patterns of data are interpreted to find ameaning and the redundant data or patterns are removedin this step.9. Discovered knowledgeIn this step, knowledge is incorporated in theperformance system and actions are taken based on theknowledge that is discovered.Table 1: Steps in Data Mining Process(Source: Larose 2014, p. 56) Data mining is gaining importance in almost every sector of the financial industry for thepurpose of analyzing data and summarizing the discovered data into valuable information(Charliepaul and Gnanadurai 2014). The main target of the banking sector is to retain customers(Chitra and Subashini 2013). This process is used for analyzing customer details for identifying
End of preview
Want to access all the pages? Upload your documents or become a member.
Related Documents
(Solved) Data Mining Process - PDFlg...
|18
|2339
|324
Data Mining - Practical Machine Learning Tools and Techniqueslg...
|8
|337
|274
Database Application Direction | Data Warehouselg...
|5
|799
|22
Data Mining: Definition, Applications, Techniques, and Challengeslg...
|13
|3378
|152
Introduction to Research: Methodology, Design, and Implementationlg...
|21
|3078
|499
Data Mining - Practical Machine Learning Tools and Techniqueslg...
|6
|1535
|230