RiskSpan Blockchain in March 23rd Issue of Asset Backed Alert

RiskSpan leaders Bernadette Kogler and Suhrud Dagli were interviewed for the March 23rd issue of Asset Backed Alert. The article is called “RiskSpan Testing Blockchain Tool” – a summary of blockchain technology that RiskSpan is developing through which loans eventually could be originated, pooled, and securitized. RiskSpan built the tool on IBM’s Hyperledger Fabric. RiskSpan…

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...

Back-Testing: Using RS Edge to Validate a Prepayment Model

Most asset-liability management (ALM) models contain an embedded prepayment model for residential mortgage loans. To gauge their accuracy, prepayment modelers typically run a back-test comparing model projections to the actual prepayment rates observed. A standard test is to run a portfolio of loans as of a year ago using the actual interest rates experienced during...

Non-Qualified Mortgage Securitization Market

Since 2015, a new tier of the private-label residential mortgage-backed securities (PLS) market has emerged, with securities collateralized by non-qualified mortgage (non-QM) loans. These securities enable mortgage lenders to serve borrowers with non-traditional credit profiles. The financial crisis ushered in a sharp reduction in mortgage credit available to certain groups of borrowers. Funding sources, such…

Loans Under $200K Prepay Slowly—But Not in Every State

In agency pools, loans with balances below $200,000 offer prepayment protection (i.e., they prepay more slowly) relative to loans with higher balances. Servicers typically segregate these loans into specified pools that trade at a premium over TBA-deliverable pools. But the prepayment protection isn’t homogenous and varies significantly by state.1 The following chart compares the S-curve…

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...