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...

Big Data in Small Dimensions: Machine Learning Methods for Data Visualization

Analysts and data scientists are constantly seeking new ways to parse increasingly intricate datasets, many of which are deemed “high dimensional”, i.e., contain many (sometimes hundreds or more) individual variables. Machine learning has recently emerged as one such technique due to its exceptional ability to process massive quantities of data. A particularly useful machine learning...

Tuning Machine Learning Models

Tuning is the process of maximizing a model’s performance without overfitting or creating too high of a variance. In machine learning, this is accomplished by selecting appropriate “hyperparameters.” Hyperparameters can be thought of as the “dials” or “knobs” of a machine learning model. Choosing an appropriate set of hyperparameters is crucial for model accuracy, but...