How Mortgages are Leveraging Machine Learning to Become Smarter

Introducing the concept of machine learning mortgage

Mortgages today are leveraging machine learning algorithms and mortgage AI technology to become smarter.

Mortgages are essential to both consumers and financial institutions. Unfortunately, the mortgage lending process is very complex and involves numerous manual interventions. Consequently, mortgage lending can be extremely tedious, time-consuming, and frustrating for both the borrowers and lenders.

However, advanced technologies such as machine learning mortgage, artificial intelligence and predictive analytics are changing this by streamlining workflows, reducing required human intervention, and automating critical decisions.

Enhanced Application Verification

Machine learning can significantly help with verification to the benefit of both lenders and borrowers. For illustration, lenders can use historical data to train predictive models that accurately estimate the applicant’s income, based on several potential factors. This can then be compared to the applicant’s stated income level to confirm if it was reasonable and accurate. This type of automated assessment can accelerate income and expense verification while also pointing out high-risk cases for manual review.

More beneficial document validation

Mortgage lending is document-intensive, and many documents are required to execute a loan. The job of lenders is to validate that information for completeness, consistency, and correctness. In such scenarios, machine learning models can be trained to do it for them. Models can automatically identify text within documents and compare it to ensure that the forms are filled correctly, letting the loan officers focus on more important tasks.

House with technology

Methodology

It enables users to automatically classify various mortgage specific documents such as payslips, bank statements, legal documents, valuation documents, affordability assessments, correspondences, and others. It ensures information is available for intelligent decision-making, eliminating risk and cost associated in manual document management by improving the time to offer and time to fund significantly.

It enables users to automatically classify various mortgage specific documents such as payslips, bank statements, legal documents, valuation documents, affordability assessments, correspondences, and others. It ensures information is available for intelligent decision-making, eliminating risk and cost associated in manual document management by improving the time to offer and time to fund significantly.

Features of built and trained proprietary machine learning models for mortgage specific documents

  • Model Deployment

    GUI-based interface to deploy models using REST APIs and simultaneously maintain metadata on deployed models.

  • Model Management

    Manage model versions across multiple deployments to ensure transparency and easy administration of multiple models.

  • Model Performance

    Monitor model performance of deployed models & get automated notifications for recalibration of models when model performance goes below a defined threshold.

  • Xplainability

    The ability for users to monitor explainability of decisions taken by the model to understand the rationale behind why the model is taking a specific decision.

  • Embedded into Workspaces

    Easily integrated with transactional systems & commonly used systems (CRM and Payment systems).

  • Role-based Access

    Advanced role-based access for model management.

Conclusion

Machine learning is a significant development that will continue to have increasing applications within the mortgage lending industry. With machine learning, mortgage lenders and underwriters are going to be more efficient, more profitable, and capable of delivering a much better customer experience.

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Posted Under: Machine Learning

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