Model Validation Programs – Optimizing Value in Model Risk Groups

Watch RiskSpan Managing Director, Tim Willis, discuss how to optimize model validation programs. RiskSpan’s model risk management practice has experience in both building and validating models, giving us unique expertise to provide very high quality validations without diving into activities and exercises of marginal value. Still want more? Take a look at our model validation… ShareTweetShare

RiskSpan Managing Director Tim WIllis

Here Come the CECL Models: What Model Validators Need to Know

As it turns out, model validation managers at regional banks didn’t get much time to contemplate what they would do with all their newly discovered free time. Passage of the Economic Growth, Regulatory Relief, and Consumer Protection Act appears to have relieved many model validators of the annual DFAST burden. But as one class of… ShareTweetShare

Join Us: Webinar – Managing Down Model Validation Costs

Register for our webinar on Managing Down Model Validation Costs, taking place on Wednesday, June 20th at 11:30am EST. Model risk managers increasingly are being asked to do more with less. Expanding model inventories and regulatory expectations are not always accompanied by commensurate increases in model validation budgets. Consequently, model risk managers have to be creative… ShareTweetShare

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

Recent FASB Updates Related to CECL

Implementing CECL has brought about a host of accounting and other technical questions. The Financial Accounting Standards Board (FASB) works with the industry through a series of meetings to identify these questions, evaluate industry feedback, and periodically issue clarifying statements. We will continuously publish summarized points of interest from these meetings as they arise. ShareTweetShare

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...ShareTweetShare

Why Model Validation Does Not Eliminate Spreadsheet Risk

Model risk managers invest considerable time in determining which spreadsheets qualify as models, which are end-user computing (EUC) applications, and which are neither. Seldom, however, do model risk managers consider the question of whether a spreadsheet is the appropriate tool for the task at hand. Perhaps they should start. Buried in the middle of page… ShareTweetShare

AML Models: Applying Model Validation Principles to Non-Models

Anti-money-laundering (AML) solutions have no business being classified as models. To be sure, AML “models” are sophisticated, complex, and vitally important. But it requires a rather expansive interpretation of the OCC/Federal Reserve/FDIC1 definition of the term model to realistically apply the term to AML solutions. Supervisory guidance defines model as “a quantitative method, system, or… ShareTweetShare