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

Applying Machine Learning to Conventional Model Validations

In addition to transforming the way in which financial institutions approach predictive modeling, machine learning techniques are beginning to find their way into how model validators assess conventional, non-machine-learning predictive models. While the array of standard statistical techniques available for validating predictive models remains impressive, the advent of machine learning technology has opened new avenues… ShareTweetShare

Applying Model Validation Principles to Machine Learning Models

Machine learning models pose a unique set of challenges to model validators. While exponential increases in the availability of data, computational power, and algorithmic sophistication in recent years has enabled banks and other firms to increasingly derive actionable insights from machine learning methods, the significant complexity of these systems introduces new dimensions of risk. When… ShareTweetShare

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

Growing Benefits of Big Data

2018 is poised to further revolutionize the way companies use Big Data to improve their financial bottom lines. The term Big Data generally refers to a dataset that is too large to be efficiently administered and evaluated by a traditional relational database. Its meaning has recently expanded, however, to include advanced technologies that can store,… ShareTweetShare

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