RiskSpan Named Category Leader in Two Chartis RiskTech Quadrants, Best of Breed in a Third

Chartis Research has classified RiskSpan among “Category leaders” in two RiskTech Quadrants® and “Best of breed” in a third Quadrant in two research reports released at the end of 2019.  RiskSpan figures into two independent reports released by Chartis in December of last year: Fixed-Income Technology Solutions, 2019: Market and Vendor Landscape and Technology Solutions for Credit Risk 2.0: Vendor Landscape, 2019. These reports summarize Chartis’s research on leading technology offerings in these two areas (fixed-income securities and credit risk) and assigns vendors into one of four sections of its RiskTech Quadrant® based on...

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Is This a “Qualified Mortgage”? Ask Fannie Mae

Who really defines what constitutes a “Qualified Mortgage”? The answer, though codified in federal regulation, is not as simple as it may appear. Updates to Regulation Z in response to the Dodd-Frank Act seem straightforward enough. Under the regulation, “Qualified Mortgage” status is presupposed so long as: 1) the loan lacks certain characteristics, including negative…

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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…

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…

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…

Permissioned Blockchains–A Quest for Consensus

Conspicuously absent from all the chatter around blockchain’s potential place in structured finance has been much discussion around the thorny matter of consensus. Consensus is at the heart of all distributed ledger networks and is what enables them to function without a trusted central authority. Consensus algorithms are designed to prevent fraud and error. With large,…

In Defense of the Federal Home Loan Banks

We succumbed to some clickbait yesterday when we encountered a Risk.net article entitled “FHLBs: safe as houses?” The subheading ominously read, “Health of huge bank funder rests on home loans and money market funds.” It caught our attention because we are well acquainted with the Federal Home Loan Bank system—an affiliated group of government-sponsored enterprises,…