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RiskSpan Edge & CRT Data

For participants in the credit risk transfer (CRT) market, managing the massive quantity of data to produce clear insights into deal performance can be difficult and demanding on legacy systems. Complete analysis of the deals involves bringing together historical data, predictive models, and deal cash flow logic, often leading to a complex workflow in multiple systems.

RiskSpan’s Edge platform (RS Edge) solves these challenges, bringing together all aspects of CRT analysis. RiskSpan is the only vendor to bring together everything a CRT analyst needs:


  • Normalized, clean, enhanced data across programs (STACR/CAS/ACIS/CIRT),
  • Historical Fannie/Freddie performance data normalized to a single standard,
  • Ability to load loan-level files related to private risk transfer deals,
  • An Agency-specific, loan-level, credit model,
  • Seamless Intex integration for deal and portfolio analysis,
  • Scalable scenario analysis at the deal or portfolio level, and
  • Vendor and client model integration capabilities.
  • Ability to load loan-level files related to private risk transfer deals.

All of these features are built into RS Edge, a cloud-native, data and analytics platform for loans and securities. The RS Edge user interface is accessible via any web browser, and the processing engine is accessible via an application programming interface (API). Accessing RS Edge via the API allows access to the full functionality of the platform, with direct integration into existing workflows in legacy systems such as Excel, Python, and R.

To tailor RS Edge to the specific needs of a CRT investor, RiskSpan is rolling out a series of Excel tools, built using our APIs, which allow for powerful loan-level analysis from the tool everyone knows and loves. Accessing RS Edge via our new Excel templates, users can:

  • Track deal performance,
  • Compare deal profiles,
  • Research historical performance of the full GSE population,
  • Project deal and portfolio performance with our Agency-specific credit model or with user-defined CPR/CDR/severity vectors, and
  • Analyze various macro scenarios across deals or a full portfolio

The web-based user interface allows for on-demand analytics, giving users specific insights on deals as the needs arise. The Excel template built with our API allows for a targeted view tailored to the specific needs of a CRT investor.

For teams that prefer to focus their time on outcomes rather than the build, RiskSpan’s data team can build custom templates around specific customer processes. RiskSpan offers support from premiere data scientists who work with clients to understand their unique concerns and objectives to integrate our analytics with their legacy system of choice.

The images are examples of a RiskSpan template for CRT deal comparison: profile comparison, loan credit score distribution, and delinquency performance for five Agency credit risk transfer deals, pulled via the RiskSpan Data API and rendered in Excel.


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Fannie Mae’s New CAS REMIC: Why REITs Are Suddenly Interested in CRT Deals

Fannie Mae has been issuing credit-risk-transfer (CRT) deals under its Connecticut Avenue Securities (CAS) program since 2013. The investor base for these securities has traditionally been a diverse group of asset managers, hedge funds, private equity firms, and insurance companies. The deals had been largely ignored by Real Estate Investment Trusts (REITs), however.

The following pie charts illustrate the investor breakdown of Fannie Mae’s CAS 2018-C06 deal, issued in October 2018. Note that REITs accounted for only 11 percent of the investor base of the Group 1 and Group 2 M-2 tranches (see note below for information on how credit risk is distributed across tranches), and just 4 percent of the Group 1 B-1 tranche.

Things began to change in November 2018, however, when Fannie Mae began to structure CAS offering as notes issued by trusts that qualify as Real Estate Mortgage Investment Conduits (REMICs). The first such REMIC offering, CAS 2018-R07, brought about a substantial shift in the investor distribution, with REITs now accounting for a significantly higher share. As the pie charts below illustrate, REITs now account for some 22 percent of the M-2 tranche investor base and nearly 20 percent of the B-1 tranche.

What Could Be Driving This Trend?

It seems reasonable to assume that REITs are flocking to more favorable tax treatment of REMIC-based structures. These will now be more simplified and aligned with other mortgage-related securities, as Fannie Mae points out. Additionally, the new CAS REMIC notes meet all the REIT income and asset tests for tax purposes, and there is a removal on tax withholding restrictions for non-U.S. investors in all tranches.

The REMIC structure offers additional benefits to REITs and other investors. Unlike previous CAS issues, the CAS REMIC—a bankruptcy-remote trust—issues the securities and receives the cash proceeds from investors. Fannie Mae pays monthly payments to the trust in exchange for credit protection, and the trust is responsible for paying interest to the investors and repaying principal less any credit losses. Since it is this new third-party trustee issuing the CAS REMIC securities, investors will be shielded from exposure to any future counterparty risk with Fannie Mae.

The introduction of the REMIC structure represents an exciting development for the CAS program and for CRT securities overall. It makes them more attractive to REITs and offers these and other traditional mortgage investors a new avenue into credit risk previously available only in the private-label market.

End Note: How Are CAS Notes Structured?

Notes issued prior to 2016 as part of the CAS program are aligned to a structure of six classes of reference tranches, as illustrated below:

Two mezzanine tranches of debt are offered for sale to investors. The structure also consists of 4 hypothetical reference tranches, retained by Fannie Mae and used for allocation of cash flows. When credit events occur, write-downs are first applied to the Fannie Mae retained first loss position. Only after the entire first loss position is written down are losses passed on to investors in mezzanine tranche debt – first M2, then M1. Loan prepayment is allocated along an opposite trajectory. As loans prepay, principal is first returned to the investors in M1 notes. Only after the full principal balance of M1 notes have been repaid do M2 note holders receive principal payments.

Beginning with the February 2016 CAS issuance (2016-C01), notes follow a new structure of seven classes of reference tranches, as illustrated below:

In addition to the two mezzanine tranches, a portion of the bottom layer is also sold to investors. This allows Fannie Mae to transfer a portion of the initial expected loss. When credit events occur, both Fannie Mae and investors incur losses. Additionally, beginning with this issuance, the size of the B tranche was increased to 100 bps, effectively increasing the credit support offered to mezzanine tranches.

Beginning with the January 2017 CAS issuance (2017-C01), notes follow a structure of eight classes of reference tranches, as illustrated below:

Fannie Mae split the B tranche horizontally into two equal tranches, with Fannie Mae retaining the first loss position. The size of the B1 tranche is 50 bps, and Fannie Mae retains a vertical slice of the B1 tranche.

Developing Legal Documents | Contract and Disclosure Tool

In a world of big data and automation, many financial institutions and legal advisors still spend an extraordinary amount of time creating the legal documentation for new financial instruments and their ongoing surveillance. RiskSpan’s Contract and Disclosure Tool, reduces the risk, time, and expenses associated with the process (patent pending).

The Tool automates the generation of a prospectus supplement, the content of which is a complex combination of static and dynamic legal language, data, tables, and images. Based on a predefined set of highly customizable rules and templates, the application dynamically converts deal-specific information from raw data files and tables into a legally compliant disclosure document. Authorized personnel can upload the data files onto the Tool’s intuitive UI, with total control and transparency over document versions and manual content changes which are automatically tracked, and which users can review, approve, or reject before finalizing the document for publication.

While there is no substitute for the legal and financial expertise of the attorneys and modelers in the financial security space, the Tool allows these professionals to make the most of their time. Rather than manually creating documentation from spreadsheets, data files, and multiple templates, users begin their analysis with a complete, pre-generated English-language document. If manual changes are further required, users can update the input data files and re-create a new document or directly and seamlessly edit the text using the application’s editing screen, which also allows users to easily visualize the changes between the versions, by highlighting content that was updated, added or deleted.

Automating the generation of legal content quantitatively decreases fees, increases productivity, and results in a much quicker turnaround, freeing up time to accommodate other business activities. The Tool’s superior computing power can turn around initial draft versions of the disclosure documents in just a few seconds!

Another feature that is difficult to overlook is the reduction of risk. It is very important that legal documentations accurately and completely reflect all of a deal’s terms and conditions. The Tool allows the legal and financial staff to focus on the deal structure, rather than manually identifying and duplicating content from prior deal templates, thereby minimizing the risks of human data errors.

The application accomplishes this in several ways. First, directly translating existing files that are used in other modeling functions ensures that model and documentation data remains aligned. Second, the static language is generated in accordance with the deal structure, leaving little room for variation. Third, a set of built-in quality control tools alerts users to missing files and data, inconsistent and erroneous structures, incorrect principal and interest payment rules, and unusual structures that require further review. Fourth, the tool keeps track of content updates and changes, and allows for version control, so users can track and review changes in document versions.

Introducing new technologies into nuanced processes can be problematic. Certainly, developing legal documents is not a one-size-fits-all proposition. Every document has its own format, criteria and legal requirements. RiskSpan’s Contract and Disclosure Tool is highly customizable to varying financial instruments and deal structures with exceptional focus on accurate legal content, quality control, and aesthetics of the final product, freeing up premium time and resources for other priorities.


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What is SOFR and What Does it Mean For You?

What is SOFR

The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by U.S. Treasury securities. As such, it will reflect an economic cost of lending and borrowing relevant to the wide array of market participants active in the financial markets. However, SOFR is fundamentally different from LIBOR. SOFR is an overnight, secured, nearly risk-free rate, while LIBOR is an unsecured rate published at several different maturities. It is a fully transaction-based rate incorporating data from transactions across three segments of the U.S. Treasury Repo market (tri-party repo, General Collateral Finance (GCF) repo and bilateral repo cleared through the Fixed Income Clearing Corporation (FICC)).[1]

The ARRC noted the need for replacement rate spreads due to the differences between rates:

Because LIBOR is unsecured and therefore includes an element of bank credit risk, it is likely to be higher than SOFR and prone to widen when there is severe credit market stress. In contrast, because SOFR is secured and nearly risk-free, it is expected to be lower than LIBOR and may stay flat (or potentially even tighten) in periods of severe credit stress. Market participants are considering certain adjustments, referenced in the fallback proposal as the applicable ‘Replacement Benchmark Spread’, which would be intended to mitigate some of the differences between LIBOR and SOFR.[2]

While the ARRC selection of SOFR as the U.S. replacement rate of choice is final, their selection is only a recommendation that LIBOR be replaced with SOFR. This creates a precarious outlook for the transition: financial institutions have to choose to take the transition seriously, and if they choose to employ rates other than SOFR, the transition could be longer and more complicated than many expect. That said, the cost benefit of choosing a different alternative reference rate is increasingly difficult to justify. With the selection of SOFR as the recommended rate, the New York Fed established an industry standard and did so in a lengthy process that included market participants and a public comment period. They also began publishing SOFR regularly on April 3, 2018.[3]

Additional steps taken by governmentsponsored enterprises (GSEs) have initiated the momentum in building out the SOFR market. In July 2018, Fannie Mae issued the first SOFR-denominated securities, leading the way for other institutions who have since followed suit.  In November 2018, the Federal Home Loan Banks (FHLBs) issued $4bn in debt tied to SOFR. The action was taken to support liquidity and help demonstrate SOFR demand to develop the SOFR market for the approximately 7,000 member institutions – banks, credit unions, and insurers – who are in the process of transitioning away from LIBOR.[4] CME Group, a derivatives and futures exchange companylaunched 3-month and 1-month SOFR futures contracts in 2018.[5] All of these steps taken to build out the market create a strong start for a rate that is already more stable than LIBORthe transaction volume underpinning SOFR rates is around $750billodaily, compared to USD LIBOR’s estimated $500 million.[6]

The ARRC has begun publishing guidance for fallback language and in the fall of 2018 published consultations on recommended language for floating rate notes and syndicated business loans.[7][8]

These initial steps to build out the necessary SOFR market put the United States ahead of the ARRC transition plan schedule and position the market well to begin SOFR implementation. However, a successful transition will require extensive engagement from other institutions. Affected institutions need to begin their transition now in order to make the gradual transition in time for the 2021 deadline.

Who Does This Transition Affect?

The transition affects any institutions that hold contracts, products, or tools that reference LIBOR and will not reach full maturity or phase out before the end of 2021. 

What Actions Do Affected Institutions Need to Take?

  1. Establish a Sponsor and Project Team:  Affected institutions need to take a phased approach to the transition away from LIBOR. Because of the need for continuous oversight, they should begin by identifying an executive sponsor and establishing a project team. The team should be responsible for all transition-related activities across the organization, including assessment of exposure and the applicability of alternative reference rates where necessary, planning the steps and timing of transition, and coordinating the implementation of transition away from LIBOR.
  2. Conduct an Impact Assessment:  The first task of the project team is to complete an impact assessment to determine the institution’s LIBOR exposure across all financial products and existing contracts that mature after 2021, as well as any related models and business processes (including third-party vendors and data providers). Regarding contracts, the team should identify and categorize all variants of legacy fallback language in existing contracts. Additionally, the assessment should analyze the risk of the LIBOR transition to the institution’s basis and operational risk and across financial holdings.
  3. Mitigate Risks:  Using results from the LIBOR exposure assessment, the project team should develop a plan running through 2021 to prioritize transition activities in a way that best mitigates risk on LIBOR exposure, and communicates the transition activities to employees and clients with ample time for them to learn about and buy into the transition objectives. 

  4. Prepare new products and tools linked to alternative reference rates: This mitigates risk by limiting the number of legacy exposures that will still be in effect in 2021 and creates a clear direction for transition activities. New references may include financial instruments and products, contract language, models, pricing, risk, operational and technological processes and applications to support the new rates.
  5. Develop and Implement Transition Contract Terms: In legacy contracts that will mature after 2021, the project team will need to amend contracts and fallback language. The ARRC has begun to provide guidance for amendments or transitions related to some financial products and will continue to publish legacy transition guidance as it fulfills its mandate. Where necessary, products must move to ARRs.
  6. Update Business Processes: Based on the impact assessment, various business processes surrounding the management of interest rate changes, including those built into models and systems will require updating to accommodate the switch away from LIBOR. For new products utilizing the new index rate, procedures, processes and policies will need to be established and tested before rollout to clients.

  7. Manage Change and Communicate:  The project team will need to develop educational materials explaining specific changes and their impacts to stakeholders. The materials must be distributed as part of an outreach strategy to external stakeholders, including clients and investors, as well as rating agencies and regulatory bodies. The outreach strategy should help to ensure that the transition message is consistent and clear as it is communicated from executives and board members to operational personnel, other stakeholders and outer spheres of influence. 

  8. Test: Financial institutions will want to prepare for regulatory oversight by testing business processes in advance. Regulators may look for documentation of the processes used to identify and remediate LIBOR risks and any risk exposure that has not been completed.

1Federal Reserve Bank of New York. “Secured Overnight Financing Rate Data.”, Accessed November 2018.

Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new originations of LIBOR syndicated business loans,” 24 September 2018., Accessed November 2018.

Federal Reserve Bank of New York. “Statement Introducing the Treasury Repo Reference Rates,” 3 April 2018., Accessed November 2018.

4Guida, Victoria. “Federal Home Loan Banks boost LIBOR replacement with $4B debt issuance,” Politico. 13 November 2018., Accessed November 2018.

CME Group. “Secured Overnight Financing Rate (SOFR) Futures.”, Accessed November 2018.

Graph: LSTA. “LIBOR and the Loan Market.” 24 April 2018., Accessed November 2018.

Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new issuances of LIBOR floating rate notes,” 24 September 2018., Accessed November 2018.

Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new originations of LIBOR syndicated business loans,” 24 September 2018., Accessed November 2018.

16 Federal Reserve Bank of New York. “Minutes,” Alternative Reference Rates Committee (ARRC). 31 October 2017., Accessed November 2018.

RiskSpan VQI: Current Underwriting Standards – Quarter 4 2018

The RiskSpan Vintage Quality Index (“VQI”) rose above, and continued to stay above, 100 in the last quarter of 2018, reaching its highest point in the last decade in October of 2018. The spike was driven by a roughly 2% increase in Cash-out Refinances and a 1.5% increase in investor occupied housing. In the last quarter, RiskSpan onboarded the FNMA and FHLMC daily loan level issuance data onto our Edge Platform, and has begun generating the VQI with our Historical Analytics API. Values for the some historical months of the VQI were re-estimated using the new data sources.

RiskSpan introduced the VQI in 2015 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 a particular vintage’s underwriting standards, which tend to shift over time.

The VQI is a function of the average number of risk layers associated with a loan originated during a given month. It is computed using the loan-level historical data released by the GSEs in support of their Credit Risk Transfer initiatives (CRT data) for months prior to December 2005, and using loan level disclosure data supporting MBS issuances through today. The value is then normalized such that January 1, 2003 has an index value of 100. The peak of the index, a value of 139 in December 2007, indicates that loans issued in that month had an average risk layer factor 39% greater (i.e., loans issued that month were 39% riskier) than loan originated during 2003. In other words, lower VQI values indicate tighter underwriting standards (and vice-versa).

Build-Up of VQI

The following chart illustrates how each of the following risk layers contributes to the overall VQI:

  • Loans with low credit scores (FICO scores below 660)
  • Loans with high loan-to-value ratios (over 80 percent)
  • Loans with subordinate liens
  • Loans with only one borrower
  • Cash-out refinance loans
  • Loans secured by multi-unit properties
  • Loans secured by investment properties
  • Loans with high debt-to-income ratios (over 45%)
  • Loans underwritten based on reduced documentation
  • Adjustable rate loans

Analytical and Data Assumptions

Population assumptions:

  • Issuance Data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose are also excluded. These loans do not represent credit availability in the market, as they likely would not have been originated today if not for the existence of HARP.

Data Assumptions:

  • Freddie Mac data goes back to December 2005. Fannie Mae data only goes back to December 2014.
  • Certain Freddie Mac data fields were missing prior to June 2008.

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

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Note: The analysis in this blog post was developed using RiskSpan’s Edge Platform. The RiskSpan Edge Platform is a module-based data management, modeling, and predictive analytics software platform for loans and fixed-income securities. Click here to learn more.[/vc_column_text][/vc_column][/vc_row]

What is LIBOR and why is it Going Away?

What is LIBOR?

The London Interbank Offered Rate (LIBOR) is a reference rate, and over time since the 1980s has become the dominant rate for most adjustable-rate financial products. A group of banks (panel banks) voluntarily report the estimated transaction cost for unsecured bank-to-bank borrowing terms ranging from overnight to one year for various currencies.

The number of currencies and maturities has fluctuated over time, but LIBOR is currently produced across seven maturities: overnight/spot, one week, one month, two months, three months, six months and one year. LIBOR rates are produced for the American dollar, the British pound sterling, the European euro, Japanese yen, and the Swiss franc, resulting in the current 35 rates.[1][2] The aggregated calculations behind the rates are supposed to reflect the average of what banks believe they would have to pay to borrow currency or the cost of funds for a specified period. However, because the contributions are voluntary, and the rates submitted are a subjective assessment of probable cost, LIBOR indices do not reflect actual transactions.

LIBOR rates became heavily used in trading in the 1980s, officially launched by the British Bankers Association (BBA) in 1986 and regulated by the Financial Conduct Authority (FCA), the independent UK body that regulates financial firms, since April 2013.[3] Until 2014, LIBOR was developed by a group of UK banks, under the BBA. The Intercontinental Exchange Benchmark Administration (ICE) took over administration of the rate in 2014 in an effort to give the rate credible internal governance and oversight – ICE created third-party oversight, which resolved the BBA’s inherent conflict of interest in generating a sound rate while also protecting its member institutions.

Why is LIBOR Going Away?

International investigations into LIBOR began in 2012 and revealed widespread efforts to manipulate the rates for profit, with issues discovered as far back as 2003. The investigations resulted in billions of dollars in fines for involved banks globally and jail time for some traders. More recently, in October 2018, a Deutsche Bank trading supervisor and derivatives trader were convicted of conspiracy and wire fraud in relation to LIBOR rigging.[4]

The scandal challenged the validity of LIBOR and deterred panel banks from continuing their involvement in LIBOR generation. Because LIBOR rates are collected by voluntary contribution, the number of banks contributing, and therefore also the number of underlying transactions, have waned in recent years. In July 2017, Andrew Bailey, Chief Executive of the FCA announced that LIBOR rates would only be formally sustained by the FCA through the end of 2021, due to limited market activity around LIBOR benchmarks and the declining contributions of panel banks. The FCA has negotiated with current panel banks for their agreement to continue contributing data towards LIBOR rate generation through the end of 2021.[5]

Even without the challenge of collecting contributions from panel banks, many regulators have expressed concerns with the representative scale of LIBOR and related issues of instability. The market of products referencing LIBOR dwarfs the transactions that LIBOR is supposed to represent. The New York Fed approximated that underlying transaction volumes for USD LIBOR range from $250 million to $500 million, while exposure for USD LIBOR as of the end of 2016 was nearly $200 trillion.[6]

What Solution are Regulators Proposing?

In 2014, the Board of Governors of the Federal Reserve System and the Federal Reserve Bank of New York (New York Fed) convened the Alternative Reference Rates Committee (ARRC) in order to identify best practices for alternative reference rates and contract robustness, develop an adoption plan, and create an implementation plan with metrics of success and a timeline. The Committee was created in the wake of the LIBOR scandals, with the intention of verifying some alternatives, though no formal change in LIBOR was announced until 2017. The Federal Reserve reconstituted this board to include a broader set of market participants in March 2018 with the updated objective of developing a transition plan away from LIBOR and providing guidance on how affected parties can address risks in legacy contracts language that reference LIBOR.

In June 2017, the ARRC announced the Secure Overnight Financing Rate (SOFR) as its recommended alternative rate, and the New York Fed began publishing the rate on April 3, 2018. In October 2017, the ARRC adopted a “Paced Transition Plan” with specific steps and timelines designed to encourage use of its recommended rate.[7]

The transition away from LIBOR impacts most institutions dealing in floating rate instruments. Stay updated with the RiskSpan blog for future LIBOR updates.


1 Kiff, John. “Back to Basics: What is LIBOR?” International Monetary Fund. Accessed November 2018. December 2012., Accessed November 2018.

“LIBOR – current LIBOR interest rates.” Global Rates., Accessed November 2018.

Bailey, Andrew. “The Future of LIBOR.” Financial Conduct Authority. 27 July 2017., Accessed November 2018

4 “Two Former Deutsche Bank Traders Convicted for Role in Scheme to Manipulate a Critical Global Benchmark Interest Rate.” U.S. Department of Justice press release. 17 October 2018., Accessed November 2018.

Bailey, Andrew. “The Future of LIBOR.” Financial Conduct Authority. 27 July 2017., Accessed November 2018.

6 Alternative Reference Rates Committee. “Second Report.” Federal Reserve Bank of New York. March 2018., Accessed November 2018.

Alternative Reference Rates Committee. Federal Reserve Bank of New York., Accessed November 2018.

Choosing a CECL Methodology | Doable, Defensible, Choices Amid the Clutter

CECL advice is hitting financial practitioners from all sides. As an industry friend put it, “Now even my dentist has a CECL solution.”

With many high-level commentaries on CECL methodologies in publication (including RiskSpan’s ), we introduce this specific framework to help practitioners eliminate ill-fitting methodologies until one remains per segment. We focus on the commercially available methods implemented in the CECL Module of our RS Edge Platform, enabling us to be precise about which methods cover which asset classes, require which data fields, and generate which outputs. Our decision framework covers each asset class under the CECL standard and considers data availability, budgetary constraints, value placed on precision, and audit and regulatory scrutiny.

Performance Estimation vs. Allowance Calculations

Before evaluating methods, it is clarifying to distinguish performance estimation methods from allowance calculation methods (or simply allowance calculations). Performance estimation methods forecast the credit performance of a financial asset over the remaining life of the instrument, and allowance calculations translate that performance forecast into a single allowance number.

There are only two allowance calculations allowable under CECL: the discounted cash flow (DCF) calculation (ASC 326-20-30-4), and the non-DCF calculation (ASC 326-20-30-5). Under the DCF allowance calculation, allowance equals amortized cost minus the present value of expected cash flows. The expected cash flows (the extent to which they differ from contractual cash flows) must first be driven by some performance estimation method. Under the non-DCF allowance calculation, allowance cumulative expected credit losses of amortized cost (roughly equal to future principal losses). These future losses of amortized cost, too, must first be generated by a performance estimation method.

Next, we show how to select performance estimation methods, then allowance calculations.

Selecting Your Performance Estimation Method

Figure 1 below lays out the performance estimation methods available in RiskSpan’s CECL Module. We group methods into “Practical Methods” and “Premier Methods.” In general, Practical Methods calculate average credit performance from a user-selected historical performance data set and extrapolate those historical averages – as adjusted by user-defined management adjustments for macroeconomic expectations and other factors – across the future life of the asset. When using a Practical Method, every instrument in the same user-defined segment will have the same allowance ratio.

Premier Methods involve statistical models built on large performance datasets containing instrument-level credit attributes, instrument-level performance outcomes, and contemporaneous macroeconomic data. While vendor-built Premier Methods come pre-built on large industry datasets, they can be tuned to institution-specific performance if the user supplies performance data. Premier Methods take instrument-level attributes and forward-looking macroeconomic scenarios as inputs and generate instrument-level, macro-conditioned results based on statistically valid methods. Management adjustments are possible, but the model results already reflect the input macroeconomic scenario(s).

Check marks in Figure 1 indicate the class(es) of financial asset that each performance estimation method covers. Single checkmarks (✔) indicate methods that require the user to provide historical performance data. Double checkmarks (✔✔) indicate methods that, at the user’s option, can be executed using historical performance data from industry sources and therefore do not require the customer to supply historical performance data. All methods require the customer to provide basic positional data as of the reporting date (outstanding balance amounts, the asset class of each instrument, etc.)

Figure 1 – Performance Estimation Methods in RiskSpan’s CECL Module

[1] Commercial real estate
[2] Commercial and industrial loans

To help customers choose their performance estimation methods, we walk them through the decision tree shown in Figure 3. These steps to select a performance estimation method should be followed for each portfolio segment, one at a time. As shown, the first step to shorten the menu of methods is to choose between Practical Methods and Premier Methods. Premier Methods available today in the RS Edge Platform include both methods built by RiskSpan (prefixed RS) and methods built by our partner, Global Market Intelligence (S&P).

The choice between Premier Methods and Practical Methods is primarily a tradeoff between instrument-level precision and scientific incorporation of macroeconomic scenarios on the Premier side versus lower operational costs on the Practical side. Because Premier Models produce instrument-specific forecasts, they can be leveraged to accelerate and improve credit screening and pricing decisions in addition to solving CECL. The results of Premier Methods reflect macroeconomic outlook using consensus statistical techniques, whereas Practical Methods generate average, segment-level historical performance that management then adjusts via Q-Factors. Such adjustments may not withstand the intense audit and regulatory scrutiny that larger institutions face. Also, implicit in instrument-level precision and scientific macroeconomic conditioning is that Premier Methods are built on large-count, multi-cycle, granular performance datasets. While there are Practical Methods that reference third-party data like Call Reports, Call Report data represents a shorter economic period and lacks granularity by credit attributes.

The Practical Methods have two advantages. First, they easier for non-technical stakeholders to understand. Secondly, license fees for Premier Methods are lower than for Practical Methods.

Suppose that for a particular asset class, an institution wants a Premium Method. For most asset classes, RiskSpan’s CECL Module selectively features one Premier Method, as shown Figure 1. In cases where the asset class is not covered by a Premier Method in Edge, the next question becomes: does a suitable, affordable vendor model exist? We are familiar with many models in the marketplace, and can advise on the benefits, drawbacks, and pricing of each. Vendor models come with explanatory documentation that institutions can review pre-purchase to determine comfort. Where a viable vendor model exists, we assist institutions by integrating that model as a new Premier Method, accessible within their CECL workflow. Where no viable vendor model exists, institutions must evaluate their internal historical performance data. Does it contain  enough instruments, span enough time ,and include enough fields  to build a valid model? If so, we assist institutions in building custom models and integrating them within their CECL workflows. If not, it’s time a begin or continue a data collection process that will eventually support modeling, and in the meantime, apply a Practical Method.

To choose among Practical Methods, we first distinguish between debt securities and other asset classes. Debt securities do not require internal historical data because more robust, relevant data is available from industry sources. We offer one Practical Method for each class of debt security, as shown in Figure 1.

For asset classes other than debt securities, the next step is to evaluate internal data. Does it represent (segment-level summary data is fine for Practical Methods) and to drive meaningful results? If not, we suggest applying the Remaining Life Method, a method that has been showcased by regulators and that references Call Report data (which the Edge platform can filter by institution size and location). If adequate internal data exists, eliminate methods that are not asset class-appropriate (see Figure 1) or that require specific data fields the institution lacks. Figure 2 summarizes data requirements for each Practical Method, with a tally of required fields by field type. RiskSpan can provide institutions with detailed data templates for any method upon request. From among the remaining Practical Methods, we recommend institutions apply this hierarchy:

  • Vintage Loss Rate: This method makes the most of recent observations and datasets that are shorter in timespan, whereas the Snapshot Loss Rate requires frozen pools to age substantially before counting toward historical performance averages. The Vintage Loss Rate explicitly considers the age of outstanding loans and leases and requires relatively few data fields.
  • Snapshot Loss Rate: This method has the drawbacks described above, but for well-aged datasets produces stable results and is a very intuitive and familiar method to financial institution stakeholders.
  • Remaining Life: This method ignores the effect of loan seasoning on default rates and requires user assumptions about prepayment rates, but it has been put forward by regulators and is a necessary and defensible option for institutions who lack the data to use the methods above.

Figure 2 – Data Requirements for Practical Methods

(Number of Data Fields Required)

[3] Denotes fields required to perform method with customer’s historical performance data. If the customer’s data lacks the necessary fields, alternatively this method can be performed using Call Report data.

Figure 3 – Methodology Selection Framework

Selecting Your Allowance Calculation

After selecting a performance estimation method for each portfolio segment, we must select our corresponding allowance calculations.

Note that all performance estimation methods in RS Edge generate, among their outputs, undiscounted expected credit losses of amortized cost. Therefore, users can elect the non-DCF allowance calculation for any portfolio segment regardless of the performance estimation method. Figure 5 shows this.

A DCF allowance calculation requires the elements shown in Figure 4. Among the Premier (performance estimation) Methods, RS Resi, RS RMBS, and RS Structured Finance require contractual features as inputs and generate among their outputs the other elements of a DCF allowance calculation. Therefore, users can elect the DCF allowance calculation in combination with any of these methods without providing additional inputs or assumptions. For these methods, the choice between the DCF and non-DCF allowance calculation often comes down to anticipated  impact on allowance level.

The remaining Premier Methods to discuss are the S&P commercial and industrial loans (C&I) – which covers all corporate entities, financial and non-financial, and applies to both loans and bonds – and the S&P commercial real estate (CRE) method. These methods do not require all the instruments’ contractual features as inputs (an advantage in terms of reducing the input data requirements). They project periodic default and LGD rates, but not voluntary prepayments or liquidation lags. Therefore, users provide additional contractual features as inputs and voluntary prepayment rate and liquidation lag assumptions. The CECL Module’s cash flow engine then integrates the periodic default and LGD rates produced by the S&P C&I and CRE methods, together with user-supplied contractual features and prepayment and liquidation lag assumptions, to produce expected cash flows. The Module discounts these cash flows according to the CECL requirements and differences the present values from amortized cost to calculate allowance. In considering this DCF allowance calculation with the S&P performance estimation methods, users typically weigh the impact on allowance level against the task of supplying the additional data and assumptions.

To use a DCF allowance calculation in concert with a Practical (performance estimation) Method requires the user to provide contractual features (up to 20 additional data fields), liquidation lags, as well as monthly voluntary prepayment, default, and LGD rates that reconcile to the cumulative expected credit loss rate from the performance estimation method. This makes the allowance a multi-step process. It is therefore usually simpler and less costly overall to use a Premier Method if the institution wants to enable a DCF allowance . The non-DCF allowance calculation is the natural complement to the Practical Methods.

Figure 4 – Elements of a DCF Allowance Calculation

I believe the S&P ECL approach is always (even with added prepayment info) a method closely related to, but not a discounted cash flow method, since the allowance for credit losses in S&P approach is calculated directly from the expected credit losses and not as amortized cost minus(-) present value of future cash flows. But this is good since it requires less inputs and easier to relate to macro-economic factors than is a pure DCF. This is consistent with Figure 5.

Figure 5 – Allowance Calculations Compatible with Each Performance Estimation Method

Once you have selected a performance estimation method and allowance calculation method for each segment, you can begin the next phase of comparing modeled results to expectations and historical performance and tuning model settings accordingly and management inputs accordingly. We are available to discuss CECL methodology further with you; don’t hesitate to get in touch!

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Blockchain and Structured Finance

Blockchain has the potential to revolutionize the financial services industry, in particular structured finance, and is rapidly becoming more of a when than an if. A main reason for the failure of the private-label residential mortgage-backed securities market to return to pre-crisis levels is due to a failure in trust, but this stalled market is ripe for innovations.

Why Blockchain?

Today’s model for mortgage data exchange is based on an outdated notion of what is technologically feasible. The servicer’s database is still thought of as a stand alone system-of-record and the investor’s database as a downstream applications that needs to rely on, reconcile, and make sense of loan-level ‘tapes generated by the system-of-record.

This model of a single system-of-record housed with the servicer could be transformed into a blockchain, with every detail of every mortgage and all subsequent transactions captured and distributed to investors. With this new model, investor reporting as it exists today would cease to exist.

This new method would instantly update investors with borrow activity, such as refinancing, prepayment, and rejected payments. On a blockchain, these transactions are a sequence that everyone can decipher.

Using Blockchain to Garner Trust in the PLS Market

Information asymmetry is consistently a problem for many in the PLS space, with many transactions having 10 or more parties contributing to verifying and validating data, documents, or cash flows in some way. Blockchain can help to overcome this asymmetry and among other challenges, share loan-level data with investors, re-envision the due diligence process, and modernize document custody, by allowing private blockchains to share information and document access with relevant parties.

The current steps for the due-diligence process are representative of the lack of trust in the PLS market. Increased transparency, using blockchain technology, could help to restore some trust and make the process run with less resistance.  Automation can streamline the due-diligence process, taking out the 100% file review that is currently required, and adding this to a secure blockchain only available to select parties. If reconciliations are deemed necessary for an individual loan file, the results could be automated and added to this blockchain.

Blockchain and Consensus

Talk about implementing blockchain into the realm of structured finance cannot ignore the issue of consensus, something at the heart of all distributed-ledger systems. Private (or ‘permissioned’) blockchains are designed for a specific business purpose, so achieving consensus requires data posted to the blockchain to be verified in an automated way by all parties relevant to the transaction.

Much of blockchain’s appeal is bound up in the promise of an environment in which deal participants can gain reasonable assurance that their counterparts are disclosing information that is both accurate and comprehensive. Visibility is an important component of this, but ultimately, achieving consensus that what is being done is what ought to be done will be necessary in order to fully eliminate redundant functions in business processes and overcome information asymmetry in the private markets. Sophisticated, well-conceived algorithms that enable private parties to arrive at this consensus in real time will be key.

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CECL: DCF vs. Non-DCF Allowance — Myth and Reality

FASB’s CECL standard allows institutions to calculate their allowance for credit losses as either “the difference between the amortized cost basis and the present value of the expected cash flows” (ASC 326-20-30-4) or “expected credit losses of the amortized cost basis” (ASC 326-20-30-5). The first approach is commonly called the discounted cash flow or “DCF approach” and the second approach the “non-DCF approach.” In the second approach, the allowance equals the undiscounted sum of the amortized cost basis projected not to be collected. For the purposes of this post, we will equate amortized cost with unpaid principal balance.

A popular misconception – even among savvy professionals – is that a DCF-based allowance is always lower than a non-DCF allowance given the same performance forecast. In fact, a DCF allowance is sometimes higher and sometimes lower than a non-DCF allowance, depending upon the remaining life of the instrument, the modeled recovery rate, the effective interest rate (EIR), and the time from default until recovery (liquidation lag). Below we will compare DCF and non-DCF allowances while systematically varying these key differentiators.

Our DCF allowances reflect cash inflows that follow the SIFMA standard formulas. We systematically vary time to maturity, recovery rate, liquidation lag and EIR to show their impact on DCF vs. non-DCF allowances (see Table 1 for definitions of these variables). We hold default rate and voluntary prepayment rate constant at reasonable levels across the forecast horizon. See Table 2 for all loan features and behavioral assumptions held constant throughout this exercise.

For clarity, we reiterate that the DCF allowances we will compare to non-DCF allowances reflect amortized cost minus discounted cash inflows, per ASC 326-20-30-4. A third approach, which is unsound and therefore excluded, is the discounting of accounting losses. This approach will understate expected credit losses by using the interest rate to discount principal losses while ignoring lost interest itself.

Table 1 – Key Drivers of DCF vs. Non-DCF Allowance Differences (Systematically Varied Below)

Variable Definitions and Notes
Months to Maturity Months from reporting date until last scheduled payment
Effective Interest Rate (EIR) The rate of return implicit in the financial asset. Per CECL, this is the rate used to discount expected cash flows when using the DCF approach and, by rule, is calculated using the asset’s contractual or prepay-adjusted cash flows. In this exercise, we set unpaid principal balance equal to amortized cost, so the EIR is the same assuming either contractual or prepay-adjusted cash flows and matches the instrument’s note rate.
Liquidation Lag (Months) Months between first missed payment and receipt of recovery proceeds
Recovery Rate Net cash inflow at liquidation, divided by the principal balance of the loan at the time it went into default. Note that 100% recovery will not include recovery of unpaid interest.


Table 2 – Loan Features and Behavioral Assumptions Held Constant

Book Value on Reporting Date Par

(Amortized Cost = Unpaid Principal Balance)

Performance Status on Reporting Date Current
Amortization Type Level pay, fully amortizing, zero balloon
Conditional Default Rate (Annualized) 0.50%
Conditional Voluntary Prepayment Rate (Annualized) 10.00%


Figure 1 compares DCF versus non-DCF allowances. It is organized into nine tables, covering the landscape of loan characteristics that drive DCF vs. non-DCF allowance differences. The cells of the tables show DCF allowance minus Non-DCF allowance in basis points. Thus, positive values mean that the DCF allowance is greater.


  • Tables A, B and C show loans with 100% recovery rates. For such loans, ultimate recovery proceeds match exposure at default. Under the non-DCF approach, as long as recovery proceeds eventually cover principal balance at the time of default, allowance will be zero. Accordingly, the non-DCF allo­wance is 0 in every cell of tables A, B and C. Longer liquidation lags, however, diminish present value and thus increase DCF allowances. The greater the discount rate (the EIR), the deeper the hit to present value. Thus, the DCF allowance increases as we move from the top-left to the bottom-right of tables A, B and C. Note that even when liquidation lag is 0, 100% recovery still excludes the final month’s interest, and a DCF allowance (which reflects total cash flows) will accordingly reflect a small hit. Tables A, B and C differ in one respect – the life of the loan. Longer lives translate to greater total defaulted dollars, greater amounts exposed to the liquidation lags, and greater DCF allowances.
  • Tables G, H and I show loans with 0% recovery rates. While 0% recovery rates may be rare, it is instructive to understand the zero-recovery case to sharpen our intuitions around the comparison between DCF and non-DCF allowances. With zero recovery proceeds, the loans produce only monthly (or periodic) payments until default. Liquidation lag, therefore, is irrelevant. As long as the EIR is positive and there are defaults in payment periods besides the first, the present value of a periodic cash flow stream (using EIR as the discount rate) will exceed cumulative principal collected. Book value minus the present value of the periodic cash flow stream, therefore, will be less than than the cumulative principal not collected, and thus DCF allowance will be lower. Appendix A explains why this is the case. As Tables G, H and I show, the advantage (if we may be permitted to characterize a lower allowance as an advantage) of the DCF approach on 0% recovery loans is greater with greater discount rates and greater loan terms.
  • Tables D, E and F show a more complex (and more realistic) scenario where the recovery rate is 75% (loss-given-default rate is 25%). Note that each cell in Table D falls in between the corresponding values from Table A and Table G; each cell in Table E falls in between the corresponding values from Table B and Table H; and each cell in Table F falls in between the corresponding values from Table C and Table I. In general, we can see that long liquidation lags will hurt present values, driving DCF allowances above non-DCF allowances. Short (zero) liquidation lags allow the DCF advantage from the periodic cash flow stream (described above in the comments about Tables G, H and I) to prevail, but the size of the effect is much smaller than with 0% recovery rates because allowances in general are much lower. With moderate liquidation lags (12 months), the two approaches are nearly equivalent. Here the difference is made by the loan term, where shorter loans limit the periodic cash flow stream that advantages the DCF allowances, and longer loans magnify the impact of the periodic cash flow stream to the advantage of the DCF approach.

Figure 1 – DCF Allowance Relative to Non-DCF Allowance (difference in basis points)

Liquidation Lag Table


  • Longer liquidation lags will increase DCF allowances relative to non-DCF allowances as long as recovery rate is greater than 0%.
  • Greater EIRs will magnify the difference (in either direction) between DCF and non-DCF allowances.
  • At extremely high recovery rates, DCF allowances will always exceed non-DCF allowances; at extremely low recovery rates, DCF allowances will always be lower than non-DCF allowances. At moderate recovery rates, other factors (loan term and liquidation lag) make the difference as to whether DCF or non-DCF allowance is higher.
  • Longer loan terms both a) increase allowance in general, by exposing balances to default over a longer time horizon; and b) magnify the significance of the periodic cash flow stream relative to the liquidation lag, which advantages DCF allowances.
    • Where recovery rates are extremely high (and so non-DCF allowances are held low or to zero) the increase to defaults from longer loan terms will drive DCF allowances further above non-DCF allowances.
    • Where recovery rates are moderate or low, the increase to loan term will lower DCF allowances relative to non-DCF allowances.[1]

Note that we have not specified the asset class of our hypothetical instrument in this exercise. Asset class by itself does not influence the comparison between DCF and non-DCF allowances. However, asset class (for example, a 30-year mortgage secured by a primary residence, versus a five-year term loan secured by business equipment) does influence the variables (loan term, recovery rate, liquidation lag, and effective interest rate) that drive DCF vs. non-DCF allowance differences. Knowledge of an institution’s asset mix would enable us to determine how DCF vs. non-DCF allowances will compare for that portfolio.

Appendix A:

The present value of a periodic cash flow stream, as discounted per CECL at the Effective Interest Rate (EIR), will always exceed cumulative principal collected when the following conditions are met: recovery rate is 0%, EIR is positive, and there are defaults in payment periods other than the first.

To understand why this is the case, note that the difference between the present value of cash flows and cumulative principal collected has two components: cumulative interest collected, which accrues to the present value of cash flows but not cumulative principal collected, and the cumulative dollar impact of discounting future cash flows, which lowers present value but does not touch cumulative principal collected. The present value of cash flows will exceed cumulative principal collected when the interest impact exceeds the discounting impact. The interest impact is always greater in the early months of a loan forecast because interest makes up a large share of total payment and value lost to discounting is minimal. As the loan ages, the interest share diminishes and the discount impact grows. In the pristine case, where book value equals unpaid principal balance and defaults are zero, the discount effect will finally catch up to the interest effect with the final payment. The present value of the total cash flow stream will thus equal the cumulative principal collected and equal the beginning unpaid principal balance. If there are any defaults in periods later than the first, however, the discount effect can never fully catch up to the interest effect. Table 3 provides one such example.

Table 3 – Cash Flow, Principal Losses, Present Value and Allowance under 0% Recovery

Loan Features and Assumptions:

  • Reporting-date amortized cost and unpaid principal balance = $10,000
  • 5-year, annual-pay, fully amortizing loan
  • Fixed note rate (and effective interest rate) of 4%
  • 10% conditional voluntary prepayment rate, 0.50% conditional default rate, 0% recovery rate

DCF allowance

DCF allowance = $10,000 − $9,872 = $128

Non-DCF allowance = Sum of Principal Losses = $134

We make the following important notes:

  • First-period defaults effectively make the loan a smaller-balance loan and will not cause a difference between the DCF allowance and non-DCF allowance; only defaults subsequent to the first period will drive a difference between the two approaches.
  • Interest-only loans will exacerbate the advantage of DCF allowances relative to non-DCF allowances.
  • For floating-rate instruments, a projected change in coupon rate (based on the known level of the underlying index as of the reporting date) does not change the fact that DCF allowance will be lower than non-DCF allowance if the conditions of 0% recovery rate, positive EIR, and presence of non-first-period defaults are met.

Finally, the discounting approach under CECL is different from that used in finance to assess the fundamental value of a loan. A loan’s fundamental value can be determined by discounting its expected cash flows at a market-observed rate of return (i.e., the rate that links recent market prices on similar-risk instruments to the expected cash flows on those instruments.) As we have noted in other blogs, CECL’s DCF method does not produce the fundamental value of a loan.

[1] We see just one case in Figure 1 that appears to be an exception to this rule, as we compare the lower-right corner of Table D to the lower-right corner of Table E. What happens between these two cells is that the DCF allowance grows from 36.8 basis points in Table D to 58.9 basis points in Table E (a 60% increase in ratio terms), while the non-DCF allowance grows from 28.4 basis points in Table D to 50.1 basis points in Table E (a 77% increase in ratio terms). Because the allowances rise in general, the subtractive difference between them increases, but we see more rapid growth of the non-DCF allowance as we continue moving from the lower-right corner of Table E to the same corner of Table F.

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Risk-as-a-Service – Transforming Portfolio Market Risk Analytics

Watch RiskSpan Co-Founder and Chief Technology Officer, Suhrud Dagli, discuss RiskSpan’s Risk-as-a-Service offerings. RiskSpan’s market risk management team has transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speeds and costs never before possible in the industry. Still want more? Take a look at our portfolio market risk analytics page.




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