Mortgage
RiskSpan Launches New Credit Risk Model for Non-QM Loans

RiskSpan Launches New Credit Risk Model for Non-QM Loans

Updated July 17, 2026

RiskSpan has introduced Credit Model 7.1, specifically designed for non-qualified mortgage (non-QM) loans. This model has been trained on a substantial dataset of $87 billion in unpaid principal balance (UPB) and comes at a time when non-QM loan issuance surged by 97% in Q3 2025, reaching $20.9 billion.

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Why it matters

  • The new model enhances the ability of lenders to assess credit risk in the growing non-QM sector, potentially leading to more competitive loan offerings.
  • Home buyers seeking non-QM loans may benefit from improved underwriting processes, making it easier for them to secure financing.
  • Real estate investors may find increased opportunities in the non-QM market as lenders become more confident in assessing risk.

RiskSpan Launches New Credit Risk Model for Non-QM Loans

Introduction

RiskSpan, a data analytics firm specializing in risk management solutions, has recently unveiled its latest product, Credit Model 7.1, tailored specifically for non-qualified mortgage (non-QM) loans. This development comes at a significant time for the non-QM market, which has seen remarkable growth in issuance.

What are Non-QM Loans?

Non-QM loans are mortgages that do not meet the standards set by the Consumer Financial Protection Bureau (CFPB) for qualified mortgages. These loans are often sought after by borrowers who may not fit the traditional mold, such as self-employed individuals or those with irregular income. The flexibility of non-QM loans allows lenders to cater to a broader range of borrowers, which can be particularly advantageous in a dynamic real estate market.

Key Features of Credit Model 7.1

The newly launched Credit Model 7.1 is built on an extensive dataset, having been trained on $87 billion in unpaid principal balance (UPB). This substantial foundation allows the model to provide more accurate assessments of credit risk associated with non-QM loans. The model aims to enhance the underwriting process, enabling lenders to make more informed decisions when evaluating potential borrowers.

Market Context

The introduction of Credit Model 7.1 coincides with a significant uptick in non-QM loan issuance. In Q3 2025, issuance rose by 97%, reaching a total of $20.9 billion. This surge indicates a growing demand for non-QM loans, as more borrowers seek alternatives to traditional mortgage products. The increase in issuance may also reflect a broader trend in the mortgage market, where lenders are adapting to changing borrower needs and preferences.

Implications for Home Buyers and Investors

The launch of Credit Model 7.1 has several implications for home buyers and real estate investors:

  • Improved Access to Financing: With a more robust credit risk model, lenders may be more willing to extend credit to borrowers who previously might have been deemed too risky. This could lead to increased access to financing for home buyers seeking non-QM loans.
  • Competitive Loan Offerings: As lenders gain confidence in assessing credit risk through the new model, they may introduce more competitive loan products. This could benefit borrowers by providing them with better terms and rates.
  • Opportunities for Investors: Real estate investors may find new opportunities in the non-QM market as lenders become more adept at evaluating risk. This could lead to a broader range of financing options for investment properties, particularly for those that do not fit traditional lending criteria.

Conclusion

RiskSpan's Credit Model 7.1 represents a significant advancement in the assessment of credit risk for non-QM loans. As the non-QM market continues to expand, this model could play a crucial role in shaping the future of mortgage lending. Home buyers and investors alike stand to benefit from the enhanced underwriting capabilities and increased access to financing that this new model promises to deliver.

RiskSpanNon-QM LoansCredit RiskMortgage IndustryReal Estate
Prop Signal briefs are AI-assisted and human-reviewed. Sources are linked above. About our process.

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