Leading mortgage data and analytics provider RiskSpan announced the release of its Whole Loan Analytics Module on the RiskSpan Edge Platform. The module enables whole loan investors, portfolio managers, and risk managers to manage loan-level data flows and predictive models that forecast loan performance under a range of scenarios.
The off-the-shelf SaaS version supports whole loan pricing and surveillance. It enables complex forecasting analytics including geographically granular House Price scenarios and historically significant economic event scenarios. Other features and custom configurations are also available for advanced risk management use cases.
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RiskSpan’s Whole Loan Analytics Module is supported by a team of data scientists, quants, and technologists who maintain the company’s proprietary prepayment and credit models. The SaaS delivery model includes continuous feature updates.
Machine Learning for Better Whole Loan Data Management
The Edge Platform uses machine learning to normalize and standardize data from disparate data file input formats. With this technology, users may easily benchmark portfolio performance against a combination of datasets. Better data inputs also dramatically improve the accuracy of downstream analytics.
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Whole Loan Analytics in Production
Recently, a large asset manager sought to enter the whole loan market by partnering with Non-Qualified Mortgage originators and servicers. This asset manager subscribed to RiskSpan’s Edge Platform and used the Whole Loan Analytics Module to perform end-to-end tracking, analysis, forecasting, and storage of all loan data. RS Edge forecasting analytics support rate sheet validation, loan pricing and pipeline analysis. The client uses the platform to automatically load and validate new data.
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