Improve Mortgage Portfolio Management with Custom Risk Scoring Models
Are you concerned about your mortgage book of business? A portfolio that’s heavily weighted toward high-risk mortgages is a liability to a financial institution and a headache that bankers don’t want. However, help is in sight. New information technology and analytics programs help improve portfolio management with custom and ready-to-use risk scoring models.
These models go beyond traditional credit scores, using a combination of general credit scoring information and predictive analysis to provide mortgage professionals with a more accurate risk assessment for each borrower. A custom risk scoring model may help financial and mortgage loan managers save time, decrease portfolio risk and improve portfolio performance.
Predict future behavior and benchmark past performance
Risk assessment and prediction are important aspects of mortgage portfolio management. Analyzing vast amounts of credit data to find patterns of consumer behavior helps predict the risk of lending money to each customer. Today’s technology greatly reduces the time and effort required to analyze these large amounts of data. Automated software programs are also better at identifying those borrowers who may be at risk of defaulting on mortgages than manual programs have been. This is because there is less room for human error in data collection and interpretation.
One of the improvements that big data and analytics have brought to risk prediction is the broader scope of risk variables they can easily generate. Sophisticated analytics programs evaluate the credit paying patterns of individuals against hundreds of other borrowers with similar financial characteristics, allowing analysts to segment borrowers and generate custom reports.
Benefits to financial institutions
Custom risk scoring models such as the Beacon 09 Mortgage Industry Option from Equifax generate a single score, but it is based on hundreds of credit characteristics compiled from previous behavior. These characteristics are then subjected to a peer comparison, and a score is generated based on 80 different variables. This score indicates how good or bad the individual’s credit risk is for each. All of this information is combined to generate one score that represents the individual’s risk level pertaining to mortgage borrowing specifically. Portfolio managers then assign current and prospective borrowers into groups of varying risk levels based on their previous credit activity.
At the portfolio level, this more thorough risk assessment leads to better portfolio performance as mortgage defaults decline. At the branch level, the refined scoring helps loan officers offer more suitable rates to mortgage customers. Marketing departments can even use this scoring model to create more specifically targeted campaigns and pinpoint previously overlooked market segments.
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