Mortgage banks and lenders need to respond to challenges such as rising origination and operating costs, complex regulations and growing borrower expectations to remain competitive in the mortgage industry. Essential Data Models listed here can help mortgage banking decision makers overcome these challenges to offer loan products while managing risks and complying with regulations.
Business Scope of Data Models:
Successful implementation of Data Models can help Mortgage Bank executives gain the knowledge needed to take actionable decisions in the following domains:
Credit Risk Analysis
Market Transaction Analysis
Interest Rate Sensitivity Analysis
Loan Performance Analysis
Borrower Lifetime Value Analysis
Loan Product Analysis
Marketing Campaign Analysis
Data Models for Mortgage Industry
Statistical data models find applications within Mortgage Banking to provide insights in making better and faster decisions.
Data Models can be created for loan-classification, loan pricing and marketing initiatives based on the historical data on ROI, cost per loan and the borrower’s credit profile.
These models can help to accurately predict the probability of outcomes of different business strategies, perform the cost benefit analysis of different options and identify indications of misrepresentation of data.
Credit Risk Modeling
To predict the likelihood of a loan default, lenders can leverage historical data to identify a consumer’s behavior in the future.
Traditionally credit risk models take 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 subject to improvement and can be optimized to predict credit risk better by using machine learning models.
Loan-classification modeling
Loan-classification models use 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 incorporating 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.
Challenges in Implementing Data Models
Data collection methodology is not designed for data analysis.
Data is diverse and complex.
User access is complex.
The consistency, accuracy, timeliness and formats of data can become a problem.
Top Data Modeling Tools
ER/Studio – ER/Studio is an intuitive data modeling tool that supports single and multi-platform environments, with native integration for big data platforms such as – MongoDB and Hadoop Hive.
PowerDesigner – PowerDesigner is one of the leading data modeling tools with features including fully integrated models and different modeling techniques that cater to both an IT-centric and non IT-centric audience.
Erwin Data Modeler– Erwin is one of the leading data modeling solutions that provides a simple, polished user interface for a complex data environment. Erwin supports high customization and automation allowing macro language, custom data types, APIs and much more.
Oracle SQL Developer Data Modeler – Oracle data modeler is a free graphical tool to help business users with data modeling tasks. It supports working with logical, relational, physical, multi-dimensional and data type models.
Sparx Systems Enterprise Architect – Sparx Systems Enterprise Architect is a feature rich data modeling tool that helps to create, verify, share ideas, workflows, charts and data models.
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