Mortgage Banking technology involves multiple vendors and players providing specialized dedicated services across various product types such as identity, credit, income, asset, tax transcripts, AUS, appraisal, closing costs and fees, product & pricing engine, compliance, title insurance and mortgage insurance. To enable a simple end-to-end digital mortgage experience for the borrower, it is necessary for Mortgage Banks to have a data integration platform that provides secure integrations to value-added third-party vendors.
Need of Data Integration for Mortgage Banking Technology
Deliver on-demand the most up-to-date data directly from the source.
Enhance borrower experience by providing a seamless, touch less, transparent, and digital mortgage experience.
Borrowers can expect quicker loan processing and funding with access to latest and accurate information from the source.
Mortgage Banks can supplement their LOS platform with additional functionality from other providers, with an emphasis on mobile computing and better borrower tools.
Improve operational efficiencies and reduce manual intervention.
Efficient vendor collaboration along with decoupling from underlying loan processing systems.
Methodology for Data Integration
Traditionally data integration is implemented at the data layer by ETL (Extract, Transform and Load) procedure.
Extracting data from one system.
Transforming that data to match another system’s format.
Loading that data into a new system.
Although ETL tools are good for bulk data movement, getting large volumes of data, and transferring them in batch, ETL processes can involve considerable complexity.
Challenges in Data Integration for Mortgage Banking Technology
But the data integration may become challenging due to the following reasons:
The ETL effort needed to bring the data into conformance with existing relational schema is a long and tedious process.
Since Mortgage Industry is a heavily document driven space, with different systems containing different types of documents it can be extremely difficult to query all the documents as an integrated whole.
Querying across multiple data stores takes a lot of processing time and effort, while duplicate data sets are often built with little governance.
This leads to inconsistent data sets lot of replicas and their transformations.
These technical issues lead to a variety of business challenges like data quality issues, increased risks, limited analytics, high costs and inferior pricing of mortgages.
Overcoming the Challenges in Data Integration
To overcome the shortcomings of ETL process, external systems can be integrated across more distinct layers like Identity layer, Process layer and Presentation layer apart from Data layer.
Building an integrated Information Architecture that can handle data sets of known structures as well as unknown structures.
Augment the capabilities of existing data warehouses as well as leverage data center best practices that are already in place.
Changes to the IT infrastructure to provide quick access to new data and new data sources so as to reduce the ETL process involved.
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