In the age of digital mortgages, the entire mortgage operations of loan origination to loan closing to post services which is highly regulated and complex is driven by transparent analytics. Data modeling plays an important role in driving this work flow to make a seamless experience for both borrowers and lenders.
Data Models in Mortgage Industry
Statistical data models find applications within Mortgage Industry in these following areas
Credit risk management
Credit Risk Modeling
To predict the likelihood of a loan default, lenders can leverage on historical data to guess how a consumer will behave in the future.
Traditionally credit risk models takes into account the following variables:
Loan Variables such as loan amount, term, interest rate, delinquency
Borrower Profile Variables such as employment status, age, relationship status
Miscellaneous Variables and other factors such as macroeconomic and demographic data.
Credit risk models are always subjected to improvement and can be optimized to predict credit risk better by using machine learning models.
Loan-classification modeling uses historical data, loan characteristics and market variables to classify loans based on specified characteristics.
This classification gives underwriters insights on how deeply they want to review a loan.
This model can help accurately predict the probability of outcomes, increase efficiency by directing underwriter focus and identify indications of misrepresentation.
Therefore this data model can be used to drive efficiencies and improve the customer experience through faster underwriting process.
Loan Pricing Data Models
Loan pricing data models can be designed based on the historical data on ROI, cost per loan and borrower’s credit profile by incorporate risk-based pricing into the model.
An accurate data driven model can help Mortgage Banks to offer loans at competitive rates.
Incorporating risk-based pricing into their models enables Mortgage banks to align loan prices with expected risk, charging higher interest rates for higher-risk loans and lower interest rates for lower-risk loans.
Data models for marketing initiatives
A clustering model will use all the data at hand in order to establish “clusters” (groups) of borrowers based on similarities in behavior, history of loan purchases and so on.
These types of models can streamline the marketing initiatives and increase the ROI of marketing campaigns.
Data modeling organizes data to align marketing decisions, analyze marketing campaigns, and craft a foundation that provides a rationale for strategic marketing decisions.
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