A robust and successful Data Analytics functionality encompasses more than a stack of technologies and is incomplete without getting incorporated into all key decisions across origination, sales, marketing, borrower experience and other core functions. So it is necessary to integrate Data Analytics into mortgage banking to aid the decision making process.
How to integrate Data Analytics into the decision making process in Mortgage Banking
Develop an Implementation strategy across the organization
- Mortgage banks can start by developing a strategy across the entire enterprise that includes a clear understanding of the milestones aimed to be accomplished and the benchmarks to measure the rate of success.
- Mortgage banks will need to reengineer decision making in business units and functions to become more analytics-driven.
- Mortgage banks needs to adapt cross-functional processes, activities, roles and responsibilities to infuse Analytics into daily decision-making process. This involves standardization of processes and cross-leverage.
Data Collection and Gathering
- Collecting quality data during the mortgage origination process is essential.
- Data collection will become more effective with the automation and digitization of the business processes during the loan life cycle.
- With high quality data collection already established in the origination processes, accurate and reliable metrics can be built around these loans.
- Data analyst can use these source data to execute queries, create advanced analytics and train machine-learning models.
Design Operating Model
- Design an optimal operating model after internal assessment of business needs and analytics capabilities and incorporating best practices.
- Build engagement and buy-in to the operating model and test with several functions to prepare for the rollout.
- Incorporate learnings and feedback to refine the operating model.
- Create a roadmap and migration path for enterprise wise implementation of the operating model.
End to end review and optimization
- The end-to-end review process view can help design effective analytics solutions and enable targeted change management to embed them into business processes.
- The review process can help to design the data-to-insight process to optimize its own functioning and update the loan product portfolio by changing loan parameters like LTV, pricing, interest rates etc.
Major Challenges in integrating Data Analytics into Mortgage Banking
- Data velocity, quantity, and complexity typical of unstructured data can prove to be the major technological challenge.
- User adoption of analytical tools within the organization especially by users who are unable to process the data at the right speed or granularity can prove to be another major hurdle.
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