While basic versions of analytic solutions focus on building data repositories and reporting, advanced predictive and prescriptive analytics can generate powerful insights. Prescriptive Analytics which use models to specify optimal behaviors and actions can be embedded as a part of real-time decision making process in the mortgage banking domain.
Need for Prescriptive Analytics in Mortgage Banking
Prescriptive Analytics can help mortgage banks and lenders to meet the following requirements:
Advanced analytics and optimization for rate scenarios to combat rate volatility.
Agility and control in borrower centric decision making process while complying with evolving regulatory requirements.
Improve borrower experience without affecting profitability.
Scope of Prescriptive Analytics in Mortgage Banking
Mortgage banks and lenders can leverage Prescriptive Analytics in the following domains:
Prescriptive Analytics can help in identifying the pricing strategy for loan products for new and existing borrowers.
Prescriptive Analytics using deterministic data models can set loans prices that that are attractive enough for borrowers to generate new business while maintaining sufficient margins for banks to be profitable.
By capturing the benefits of information asymmetries about an existing borrower, Prescriptive models can create offers to increase cross selling opportunities to existing borrowers.
Prescriptive models can run complex models analyze scenarios and factor in business rules and constraints so the mortgage banking executives can find the best course of action among numerous possibilities.
Key decision makers in the mortgage banking industry can experiment with multiple what-if scenarios and rapidly adjust their plans for unexpected events.
These optimization models can be constantly improved by using ML algorithms, APIs, high level scripting and power visualization tools.
Channel Mix Modeling
Channel Mix Modeling is the technique which can be employed in mortgage banking to quantify the impact of several loan variable inputs on total loan volumes or profits.
Mortgage banks can improve the efficiency of loan processing and find a better mix of loans to meet borrower demand by employing Prescriptive Analytics.
Prescriptive analytics allows lenders to adjust LTV ratios, in accordance with regulations, while meetings capital requirements.
Risk modeling and analytics allows mortgage banks to analyze the loan product portfolio to forecast likely losses and make provisions for those adequately.
Value at Risk (VAR) calculation is one of the techniques using to calculate the risk factor using statistical methods and simulation.
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