Big data analytics play a vital role in analyzing the huge volumes of mortgage data stored in the data mart or the data warehouse at considerably faster rates than using traditional data mining methods. Big data analytics is offered by a number of software frameworks including the Hadoop software framework, which is an open-source framework. Such software frameworks analyze the stored mortgage data based on the distributed file systems, which can be used for analyzing both structured and unstructured mortgage industry-related data.
The framework in conjunction with an appropriate database helps to analyze mortgage-related data and create data models that can be used for analyzing the following mortgage aspects:
Mortgage risk management
Delinquency models play a vital role in predicting the number of customers who would turn delinquent in the next two to three years. Based on the results obtained from this delinquency model, mortgage industry analysts can take appropriate measures to handle the situation.
In order to construct this model, data from different diverse sources such as the customer transaction data and fees transaction data and the borrower and loan relation data are required. Such data can span up to one billion rows and can consume significant processing power when a conventional data mining technique is used. By using big data solutions, such operations can be completed within a quick span of time without consuming additional CPU power.
Identification of sub-serviced loans
Several large banks have transferred the mortgage servicing rights to subservice providers for particular types of loan that have become delinquent. One of the challenges for a mortgage service provider is to identify such loans that need to be transferred to a subservice provider. In order to perform this, mortgage business analysts have to perform a detailed analysis on the data related to the loan history, borrower history and also the data regarding loan documents that are stored in the document management systems.
An automated analytics program such as big data software can scan the different transaction logs and group the data at both the loan level and borrower level. Based on these data groups, it could become easier for the analysts to identify the loans that can be subserviced. When the process is done using the traditional method, banks and lending institutions would take several weeks to arrive at the delinquent loans that can be subserviced.
Mortgage fraud prediction
Banks and financial institutions have struggled to control the rise in mortgage fraud in spite of the huge volume of mortgage data available at their disposal. The big data analytics software can help mortgage service providers to identify potential mortgage frauds based on the zip code of the region from where the loan originated. The fraud prediction functionality would come in extremely handy during the appraisal process of the new loan application system.
As you can see, big data analytics software play a vital role in performing mortgage data analysis and generate reports at a much faster rate even when working with billions of rows of data.
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