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… ShareTweetShare

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… ShareTweetShare

Man vs. Machine: The Prepayment Modeling Story

Prepayment modeling is an established art form with a well-known functional form. Though the exact model varies from institution to institution, the basic structure of the model has not changed significantly over time. Recently, new modeling techniques, in the form of machine learning, have taken the world by storm, excelling in areas such as image… ShareTweetShare

prepayment modeling machine learning

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...ShareTweetShare

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...ShareTweetShare

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...ShareTweetShare

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...ShareTweetShare

Evaluating Supervised and Unsupervised Learning Models

Model evaluation is the process of objectively measuring how well machine learning models perform the specific tasks they were designed to do—such as predicting a stock price or appropriately flagging credit card transactions as fraud. Because each machine learning model is unique, optimal methods of evaluation vary depending on whether the model in question is “supervised” or “unsupervised.” Supervised machine learning models make specific predictions or classifications based on labeled training data, while unsupervised machine learning models seek to cluster or otherwise find patterns in unlabeled data. ShareTweetShare