RiskSpan VQI: Current Underwriting Standards – April 2018

The Vintage Quality Index continued to climb during April 2018–a reflection of rising debt-to-income ratios and falling credit scores–settling at 102.6 In 2015, RiskSpan introduced the Vintage Quality Index (VQI) as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. The idea is to provide credit modelers a way of controlling for… 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

What Data Do I Need For CECL Modeling?

Even with CECL compelling banks to collect more internal loan data, we continue to emphasize profitability as the primary benefit of robust, proprietary, loan-level data. Make no mistake, the data template we outline below is for CECL modeling. CECL compliance, however, is a prerequisite to profitability. Also, while third-party data may suffice for some components of the CECL estimate, especially in the early years of implementation, reliance on third-party data can drag down profitability. Third-party data is often expensive to buy, may be unsatisfactory to an auditor, and can yield less accurate forecasts. Inaccurate forecasts mean volatile loss reserves and excessive capital buffers that dilute shareholder profitability. An accurate forecast built on internal data not only solves these problems but can also be leveraged to optimize loan screening and loan pricing decisions. ShareTweetShare

Sample Size Requirements for CECL Modeling

With CECL implementation looming, many bankers are questioning whether they have enough internal loan data for CECL modeling. Ensuring your data is sufficient is a critical first step in meeting the CECL requirements, as you will need to find and obtain relevant third-party data if it isn’t. This article explains in plain English how to calculate statistically sufficient sample sizes to determine whether third-party data is required. More importantly, it shows modeling techniques that reduce the required sample size. Investing in the right modeling approach could ultimately save you the time and expense of obtaining third-party data. ShareTweetShare

What CECL Means To Investors

Recent updates to U.S. GAAP will dramatically change the way financial institutions incorporate credit risk into their financial statements. The new method is called the Current Expected Credit Loss (CECL) model and will take effect over the next few years. For many institutions, CECL will mean a one-time reduction in book equity and lower stated earnings during periods of portfolio growth. These reductions occur because CECL implicitly double-counts credit risk from the time of loan origination, as we will meticulously demonstrate. But for investors, will the accounting change alter the value of your shares? ShareTweetShare