Big part of digital transformation in mortgage industry drills down to the number of data driven initiatives by mortgage lenders and banks namely AI/ML efforts, predictive analytics, data modeling and CRMs. Explosive growth in terms of total data volume poses many operational and data challenges in mortgage industry.
Data challenges in mortgage industry
- The total volume of data and the variety of data generated by the data driven initiatives leads to data handling challenges since most of this growth is in unstructured data.
- Since the problem of data variety, volume and velocity is currently tackled using data warehouses and data silos, mortgage banks struggles with data access.
- Without a unified data access platform, LOs will not be able to get a single, comprehensive view of the borrower.
- Manually collecting, cleaning and organizing the incoming data is time consuming and error prone.
- If the huge amount of data collected is not harnessed to derive insights and actions, mortgage banks are under-utilizing the data with them.
Solving the Data Challenges
- Data driven cloud strategy: Mortgage banks can harness the data that is already residing within their silos by adopting a data driven cloud strategy so that advanced analytics can be applied to the large amount of structured and unstructured data to gain a 360 degree view of the borrowers and loan products.
- Automation: Manual data gathering, tracking, cleansing and classification must be automated leveraging data extraction tools and machine learning and data science techniques.
- Hybrid Cloud Computing: Hybrid cloud computing enables seamless management and distribution of data and analytics consistently across the on-premises environment and the cloud.
- AI and ML techniques: AI and ML techniques can be leveraged to automate the repetitive, predictive and stable processes involving homogeneous data and handle large amount of structured and unstructured data for analytics.
- Data Capture Tools for compliance: Intelligent Data Capture tools can be implemented to extract data fields from borrower and seller closing disclosures. These data are then automatically compared with loan estimates and process statements to check for data mismatches and execute to tolerance rules to identify any data fields which are out of compliance.
- BPA: BPA can mitigate the inherent risks involved in conventional data validation processes by using automated techniques for data gathering, automated rules- based validations, and data validations.
- Data Integration platforms: Mortgage banks can make use of data integration platforms like Salesforce Customer 360 which united marketing, sales, commerce, service, and I.T. departments and thereby created a unified customer ID to build a single view of the borrower.
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