Webinar: A Data Driven Approach to Pre-Trade Analytics & Pricing

Investors are preparing for a surge in RMBS in 2020, with expected changes to the non-QM patch.  Get ahead of the market curve and join modeling and analytics expert, Janet Jozwik, to review industry best practices for RMBS pre-trade analytics. hbspt.cta.load(2745346, '86e11fc9-48a9-429d-8ff9-dddfcc40885b', {}); Learn how RiskSpan pulls together past, present, and future for superior analysis on new RMBS...

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RS Edge for Loans & Structured Products: A Data Driven Approach to Pre-Trade and Pricing  

The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch.   PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each...

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Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products   RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products.  RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range...

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RiskSpan Adds Whole Loan Analytics to Edge Platform

RiskSpan Adds Whole Loan Analytics to Edge Platform  ARLINGTON, VA, May 20, 2019 – 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...

Whole Loan Analytics

Join Us: RiskSpan at MISMO 2018

Join RiskSpan at MISMO 2018, where our Director of Model Development Fan Zhang will present on the implementation of Machine Learning. Specifically, he will tackle appropriate cases for the implementation of Artificial Intelligence and Machine Learning, determining if this technology is suitable for a problem, and some popular languages and libraries for implementation. The overall…

Join Us: Webinar – Machine Learning in Building a Prepayment Model

Prepayment models are a key component in the valuation of mortgage-backed securities. With billions of dollars’ worth of investments hinging upon the accuracy of these models, the reliability of their predictions is of the utmost importance. Prepayment models are highly complex, and must account for a wide range of behaviors across diverse population segments. Please join…

Machine Learning Detects Model Validation Blind Spots

Machine learning represents the next frontier in model validation—particularly in the credit and prepayment modeling arena. Financial institutions employ numerous models to make predictions relating to MBS performance. Validating these models by assessing their predictions is of paramount importance, but even models that appear to perform well based upon summary statistics can have subsets of...

Data Management

Hands-On Machine Learning–Predicting Loan Delinquency

The ability of machine learning models to predict loan performance makes them particularly interesting to lenders and fixed-income investors. This expanded post provides an example of applying the machine learning process to a loan-level dataset in order to predict delinquency. The process includes variable selection, model selection, model evaluation, and model tuning. The data used...