DATA MINING IN THE MORTGAGE INDUSTRY   Term Paper ID:42058 Click to get this paper

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Paper Abstract: Considers how data mining was used in the past in the mortgage industry, and how it can be used in the future. Paper Introduction: Data Mining in the Mortgage Industry Introduction Companies operating in the mortgage industry collect a vast amount ofinformation about their current and potential future customers Thesecompanies obtain data about earnings assets criminal records spendingtendencies credit history and a host of other things that can provideinsight into the consumer\'s likelihood to successfully repay a mortgage Financial institutions recognize the value of this data and have longsought ways to take advantage of it to identify patterns that might assistin marketing efforts or prevent loans Text of the Paper: The entire text of the paper is shown below. However, the text is somewhat scrambled. We want to give you as much information as we possibly can about our papers and essays, but we cannot give them away for free. In the text below you will find that while disordered, many of the phrases are essentially intact. From this text you will be able to get a solid sense of the writing style, the concepts addressed, and the sources used in the research paper. history and a host of other things that loans to risky customers This researchconsiders data mining and can be used to puttogether information database queries Datamining can be used to discover knowledge be used to determine which types of take out second mortgages in the future data mining efforts was conducted in and it but thosewere considered too complex at Intelligent Miner was able to successfullymodel serious there has been little progress on data warehouses below However the not yet moved beyond simple queries forextracting data from In order to be effective data mining requires a company\'s various computer systems including thoserelating to customers to collectively as data mining these techniques give data to be managed andtransparent nearly impossible Meta data is the summary information about be accurate and synchronized to the changes which occur in systems fits into the larger decisionsupport system Worsley Privacy Concerns right to privacy and theright of individuals to control how later be reported to thegovernment Financial institutions can face significant equity lines of creditwhich might be used of the vast amounts of data that it gathers a lack of understanding as to thebenefits privacy issues ReferencesCarr D F November When data Boston MA Cengage Learning Pike G H December History repeated ieee org Xplore login jsp about their current and potential future customers Thesecompanies data and have longsought ways to take advantage of it allow computer programs to put togetherdisparate pieces of individual It is this identification ofpatterns occurs whenthose patterns are used That information can then be used to predict what mortgage industry had to wait until there wassufficient IT infrastructure-such institutions tended to rely on logisticalregression new IBM\'s IntelligentMiner product combined the benefit Data Mining in the Mortgage Industry In the decade to the perceiveddaunting investment in resources and hardware institutions and the potential benefit fromknowledge discovery and Decoff even when data mining is successfully usedelsewhere as well as summary levels to the term data warehouse Various methods have becauseof the sheer size and heterogeneity of the the data to be managed Worsley Without of thevast amounts of data stored in data data warehouse Through a combination of meta data analysis and from a line of credit this When a consumer conducts a transaction at any entity mortgage industry can provide valuable adopting data mining techniques Pike Conclusion Data mining offers industry This is partly due to the once-large infrastructurerequired to although care willneed to be taken to ensure Technology Retrieved October from http www mortgage- technology com mining Proceedings of the IEEE Data Mining in the Mortgage Industry Introduction Companies operating in can provideinsight into the consumer\'s likelihood to successfully its use in the mortgage industry as one from a wide variety of which occurs when a particularpattern is identified borrowershave a second mortgage in addition this ispredictive data mining Decoff Early sought to redefine the way that mortgage that time to be useful on delinquency based on million separate loans with acombined toward wider implementation of datamining in the storageand processing requirements for effective their consumer mortgage databases this may also bedue to a data warehouse Datawarehouses bring integrate historical employees vendors products inventory andfinancials companies are companiesthe ability to use data to users while at the same time they what data is in thewarehouse and is critical to the data Openness and concurrency is critical In the financial services industry privacy concerns arise information about them is used providethe basis for constitutional objections pressure toprovide information to the government in to finance terrorist activities The on currentand potential consumers However data mining has of data mining Additional education in this field can help threatens privacy Baseline Decoff P May The with the USA Patriot Act Information Today - url iel pdf arnumber Worsley C September Data warehousing in obtain data about earnings assets criminal records spendingtendencies credit to identify patterns that might assistin marketing efforts or prevent data in order to identify a pattern that separates data mining from simple to predict future behavior In the mortgageindustry data mining can types ofborrowers are likely to as data warehouses-to support extensivesearches One of the earliest approaches on the market used neural networks of neural network analysis with visualpresentation In one study since the above study was conducted the consensus isthat necessary to support datamining see the discussion knowledge prediction is significant Nonetheless many financial institutions have in the institution Oz Data Mining and Data Warehouses By bringing together all of thecomponents of a beendeveloped for retrieving information from the data warehouse Commonlyreferred data involved Data warehousesare scalable to adapt ever-increasing amounts of data warehouses data mining would be warehouses it is critical that metadata modeling users are betterable to understand how each of their could indicatethat the borrower is in financial trouble The that collectsdata about the consumer that transaction can information interms of consumers who are taking or using large significant opportunity for the mortgage industryto take advantage support it and partly due to that institutions do not expose themselves tolitigation based on plus feature story id Oz E Management information systems IAFE - Retrieved October from http ieeexplore the mortgage industry collect a vast amount ofinformation repay a mortgage Financial institutions recognize the value of this way toaddress this issue Data Mining Data mining techniques which sources and identify possiblepatterns about a particular in a set of data Knowledge prediction to their first This is knowledgediscovery Data Mining in the Mortgage Industry Data mining in the applications were scored Until that time financial a widespreadbasis A radial basis function RBF approach used by value of more than trillion John Zhao Current and Future mortgage industry Initially this was due data mining are easily within thebudgets of most financial misunderstanding of the potential that data mining offers in thisenvironment data with current data in bothdetailed able to create large repositories of data Thisis what led in ways which were previously unavailable are able to accommodatethe heterogeneous nature of the concept of data mining Because to the success of a when forexample a borrower uses funds to electronic privacy and datamining the pursuit of terrorists anddata mining in the fear of lawsuitsmight prevent some institutions from fully been slow to be adoptedby the theindustry take full advantage of data mining techniques bottom line on data mining Mortgage John G H Zhao Y March Mortgage data \'real time \' Software World - history and a host of other things that loans to risky customers This researchconsiders data mining and can be used to puttogether information database queries Datamining can be used to discover knowledge be used to determine which types of take out second mortgages in the future data mining efforts was conducted in and it but thosewere considered too complex at Intelligent Miner was able to successfullymodel serious there has been little progress on data warehouses below However the not yet moved beyond simple queries forextracting data from In order to be effective data mining requires a company\'s various computer systems including thoserelating to customers to collectively as data mining these techniques give data to be managed andtransparent nearly impossible Meta data is the summary information about be accurate and synchronized to the changes which occur in systems fits into the larger decisionsupport system Worsley Privacy Concerns right to privacy and theright of individuals to control how later be reported to thegovernment Financial institutions can face significant equity lines of creditwhich might be used of the vast amounts of data that it gathers a lack of understanding as to thebenefits privacy issues ReferencesCarr D F November When data Boston MA Cengage Learning Pike G H December History repeated ieee org Xplore login jsp about their current and potential future customers Thesecompanies data and have longsought ways to take advantage of it allow computer programs to put togetherdisparate pieces of individual It is this identification ofpatterns occurs whenthose patterns are used That information can then be used to predict what mortgage industry had to wait until there wassufficient IT infrastructure-such institutions tended to rely on logisticalregression new IBM\'s IntelligentMiner product combined the benefit Data Mining in the Mortgage Industry In the decade to the perceiveddaunting investment in resources and hardware institutions and the potential benefit fromknowledge discovery and Decoff even when data mining is successfully usedelsewhere as well as summary levels to the term data warehouse Various methods have becauseof the sheer size and heterogeneity of the the data to be managed Worsley Without of thevast amounts of data stored in data data warehouse Through a combination of meta data analysis and from a line of credit this When a consumer conducts a transaction at any entity mortgage industry can provide valuable adopting data mining techniques Pike Conclusion Data mining offers industry This is partly due to the once-large infrastructurerequired to although care willneed to be taken to ensure Technology Retrieved October from http www mortgage- technology com mining Proceedings of the IEEE If this paper is not what you are looking for, you can search again: Search for: or Click here to request an essay written just for you.

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