Linkedin    Twitter   Facebook

Get Started
Log In

Linkedin

Category: Uncategorized

RiskSpan Partners with S&P Global Market Intelligence

ARLINGTON, Va., December 5, 2018 /PRNewswire/ — Virginia-based modeling and analytics SaaS vendor RiskSpan announced today that it will be partnering with S&P Global Market Intelligence to expand the capabilities of its commercially-available RS Edge Platform.

RS Edge is a SaaS platform that integrates normalized loan and securities data, predictive models and complex scenario analytics for commercial banks, credit unions, insurance companies, and other financial institutions. The RS Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.

RiskSpan’s CECL module features broad-based methodologies covering all loan types and security types. The integration of S&P Global Market Intelligence’s C&I and CRE CECL models, built on 36 years of default and recovery data, adds loan-level, econometric models for these major asset classes from a globally recognized credit ratings institution. These enhancements further equip RiskSpan clients to navigate FASB’s impending CECL standard as well as IFRS 9 requirements.

“We’re very excited to leverage S&P Global Market Intelligence’s CECL credit models and methodologies on our SaaS platform” said RiskSpan CEO Bernadette Kogler. “Coupled with RiskSpan’s technology capabilities and risk management expertise, our CECL solution is set up to provide unmatched value to the market.”

Bob Durante, Senior Director of Risk Solutions at S&P Global Market Intelligence added, “We are pleased to offer our CECL credit models through partners such as RiskSpan. This partnership brings our best of breed CECL models directly through RiskSpan to a wide array of customers in the commercial banking, community banking, and insurance industries.”

Learn more about our CECL module here.

Get a Demo

About RiskSpan

RiskSpan simplifies the management of complex data and models in the capital markets, commercial banking, and insurance industries. We transform seemingly unmanageable loan data and securities data into productive business analytics.

About S&P Global Market Intelligence

At S&P Global Market Intelligence, we know that not all information is important—some of it is vital. Accurate, deep and insightful. We integrate financial and industry data, research and news into tools that help track performance, generate alpha, identify investment ideas, understand competitive and industry dynamics, perform valuations and assess credit risk. Investment professionals, government agencies, corporations and universities globally can gain the intelligence essential to making business and financial decisions with conviction.

S&P Global Market Intelligence a division of S&P Global (NYSE: SPGI), provides essential intelligence for individuals, companies and governments to make decisions with confidence. For more information, visit www.spglobal.com/marketintelligence.


RiskSpan Ranks in Chartis Research RiskTech 100 2019

RiskSpan is excited to announce we have ranked on the RiskTech 100 report by Chartis Research. This represents a notable rise of fourteen spots compared to 2018. The Chartis RiskTech 100 analyzes firms in the risk technology space, and serves as one of the most trusted reports for clear and reliable information about the risktech space and the exciting new developments coming out of it. This jump in the rankings represents one of the largest gains in this year’s report, and reflects RiskSpan’s focus on applying innovative technology to our core offerings. RiskSpan provides a data, modeling, and analytics Platform and Services to the finance industry – including the commercial banking, insurance, and capital markets sub-segments. Our flagship data/modeling/forecasting/valuation software, the RiskSpan Edge Platform, is a cloud-native system for hosting loan and fixed-income securities data, performing historical and predictive analytics/forecasting, and generating explanatory reports and data visualizations. RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.  

Get a Demo

For over a decade, RiskSpan has been the consulting services vendor of choice for large banking, insurance, and capital markets participants. RiskSpan data scientists, technologists, and quants have handled data management, model development, and model validation, and we have adapted our products to the mid-sized and small commercial banking and insurance sectors. talk scope risktech 100 Interested in learning more about our platform and services? Get in touch today.


RiskSpan Adds Home Equity Conversion Mortgage Data to Edge Platform

ARLINGTON, VA, September 12, 2018 — Leading mortgage data analytics provider RiskSpan added Home Equity Conversion Mortgage (HECM) Data to the library of datasets available through its RS Edge Platform. The dataset includes over half a billion records from Ginnie Mae that will expand the RS Edge Platform’s critical applications in Reverse-Mortgage Analysis. RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.

The HECM dataset is the latest in a series of recent additions to the RS Edge data libraries. The platform now holds over five billion records across decades of collection and is the solution of choice for whole loan and securities analytics. RiskSpan’s data strategy is simple. Provide our customers with normalized, tested, analysis-ready data that their enterprise modeling and analytics teams can leverage for faster, more reliable insight. We do the grunt work so that you don’t have to, said Patrick Doherty, RiskSpan’s Chief Operating Officer.  The HECM dataset has been subjected to RiskSpan’s comprehensive data normalization process for simpler analysis in RS Edge. Edge users will be able to drill down to snapshot and historical data available through the UI. Users will also be able to benchmark the HECM data against their own portfolio and leverage it to develop and deploy more sophisticated credit models.  RiskSpan’s Edge API also makes it easier-than-ever to access large datasets for analytics, model development and benchmarking. Major quant teams that prefer APIs now have access to normalized and validated data to run scenario analytics, stress testing or shock analysis. RiskSpan makes data available through its proprietary instance of RStudio and Python.

Get a Demo


RiskSpan to Offer Credit Risk Transfer Data Through Edge Platform

ARLINGTON, VA, September 6, 2018 — RiskSpan announced today its rollout of Credit Risk Transfer (CRT) datasets available through its RS Edge Platform. The datasets include over seventy million Agency loans that will expand the RS Edge platform’s data library and add key enhancements for credit risk analysis.  RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.  New additions to the RS Edge Data Library will include key GSE Loan Level Performance datasets going back eighteen years. RiskSpan is also adding Fannie Mae’s Connecticut Avenue Securities (CAS) and Credit Insurance Risk Transfer (CIRT) datasets as well as the Freddie Mac Structured Agency Credit Risk (STACR) datasets.  

Each dataset has been normalized to the same standard for simpler analysis in RS Edge. This will allow users to compare GSE performance with just a few clicks. The data has also been enhanced to include helpful variables, such as mark-to-market loan-to-value ratios based on the most granular house price indexes provided by the Federal Housing Finance Agency.  Managing Director and Co-Head of Quantitative Analytics Janet Jozwik said of the new CRT data, “Our data library is a great, cost-effective resource that can be leveraged to build models, understand assumptions around losses on different vintages, and benchmark performance of their own portfolio against the wider universe.”  RiskSpan’s Edge API also makes it easier-than-ever to access large datasets for analytics, model development and benchmarking. Major quant teams that prefer APIs now have access to normalized and validated data to run scenario analytics, stress testing or shock analysis. RiskSpan makes data available through its proprietary instance of RStudio and Python. 

get a demo


How Buyouts Drive Ginnie Mae Prepayment Speeds

Because Ginnie Mae mortgage-backed securities are backed by the full faith and credit of the U.S. government, investors are not subject to credit losses. However, the potential for non-performing loan buyouts creates an additional layer of prepayment risk. As with any prepayment, investors receive the unpaid principal balance of the loan that goes through buyout. However, for all 30-year pass-throughs with 3% and higher coupons trading above par, any prepayment (due to a buyout or otherwise) represents a loss to the investor.

So how much of a concern are buyouts for investors?

Prepayments

Prepayments for Ginnie Mae MBS are comprised of a voluntary component (the conditional repayment rate, CRR) along with an involuntary portion (the conditional buyout rate or CBR). Since FHA and VA loans, the primary collateral backing Ginnie Mae MBS, typically behave differently, we analyze their performance separately. The analysis that follows is based on all 30-year FHA and VA loans originated since 2014 that are included in Ginnie Mae pools. The chart below illustrates the dramatic convergence in speeds relative to the end of 2016 when VA loans were paying 7% to 8% faster than FHA loans.

delinquencies and buyouts.PNG

Deconstructing the overall prepayment rate reveals that the convergence is due to both a narrowing of the CRR difference along with a spike in the CBR for FHA loans beginning in June of this year.

Serious delinquencies are a leading indicator of future buyouts. Comparing the percentage of 90-day (or more) delinquencies as a percentage of the outstanding balance indicates a fairly consistent difference (54 bps on average) between FHA and VA loans, with both trending upward.

delinquencies and buyouts.PNG

Aging Effects

If we further stratify the loans based on vintage and look at the patterns as the loans age, will there be any material differences?

The 2014 vintage FHA cohort has performed poorly based on the buyout rate relative to the newer vintages. The 2016 vintage appears to be aging in a similar manner to the 2015 vintage while the early results for the 2017 cohort place it somewhere between the 2014 and 2015 vintages. All of the VA vintages have experienced fewer buyouts than their FHA counterparts. The 2016 VA cohort is the standout thus far followed by the 2015 and 2014 vintages. With only a few months of data to go on, the 2017 VA loans are outperforming the 2014 and 2015 loans, but are not as stellar as the 2016s.

delinquencies and buyouts.PNG

The patterns largely carry over to the 90-day or more delinquencies. 2014 vintage FHA loans generally show the highest serious delinquency percentage at any given age. However, the 2015 cohort has experienced a sharp uptick beginning at 27 months and, at an age of 31 months, exceeds the 2014 level. VA loans do not exhibit a meaningful difference among the vintages.

delinquencies and buyouts.PNG

Conclusion

Buyouts should be a consideration for Ginnie Mae investors, particularly for FHA loans. The analysis has shown that buyout rates are significantly higher for FHA loans relative to VA loans. With the CBR for FHA loans averaging 3.2x higher than the VA CBR over the last twelve months it needs to be factored into the investment equation.


Reviving the Private-Label RMBS Market with Improvements to the Securitization Process

Weaknesses in securitization processes for mortgage loans contributed to the financial crisis of 2007 – 2008 and have led to a decade-long stagnation in the private-label residential mortgage-backed securities (PLS) market.

Although market participants have attempted to improve known weaknesses, lack of demand for private-label RMBS reflects investors’ reluctance to re-enter the market and the need for continued improvements to securitization processes to re-establish market activity.  While significant issues still need to be addressed, promising advances have been made in the PLS market that improve information provided to investors as well as checks and balances designed to boost transaction performance.

Specifically, we are beginning to see significant improvements in the following securitization processes:

  • Due Diligence
  • Rating Agency Assessment
  • Representation and Warranty Framework and Enforcement
  • Loan Quality Standards
  • Risk Retention
  • Bondholder Communication

Enhancements to these processes in the post-crisis PLS market improve transparency; align incentives between issuers, sponsors, and investors; and may lead to increased investor trust in this market segment.

Due Diligence

The due diligence process is intended to provide the purchaser of an asset with an opportunity to assess the asset’s quality. Prior to the financial crisis, investors relied on the underwriter of the securitization (i.e., an investment bank) to perform loan-level due diligence on their behalf and assess the quality of the underlying loans. Limited information about these reviews was made available to investors. The process was opaque and did not provide investors a clear view of the quality of loans underlying a securitization.

Prior to the financial crisis, due diligence was performed on between 5% and 10% of the loans in a securitization. (Slightly larger samples were selected for Alt-A and subprime transactions.) The criteria for selecting the specific loans in the sample was generally not communicated to investors and rating agencies. Even more odd, the due diligence results were not communicated to key transaction parties (rating agencies and investors) and issuers did not disclose the results in disclosure documents.

Since the crisis, the following improvements to the due diligence process have made it more transparent:

  • While specific due diligence sample sizes have not been mandated, securitizations issued since the financial crisis have significantly increased the percentage of loans being reviewed—in many transactions, issuers have even included all loans. In two recent Prime Jumbo securitizations, Flagstar and JPMorgan Chase performed 100% due diligence on the underlying loans.
  • Rating Agencies have defined requirements for the firms that perform due diligence activities. Market participants have recommended standards for the scope of the due diligence performed. For example, the Structured Finance Industry Group (“SFIG”) has outlined general criteria for the review of credit, property valuation and regulatory compliance on loans reviewed during the due diligence.
  • Due diligence results are provided to all rating agencies under SEC Rule 17g-10. These reports detail the number of loans reviewed, due diligence findings, the number of loans dropped during the due diligence process, and the rationale behind dropping them. The reports summarize grades assigned to each loan based on rating agency criteria and are made available on the Securities and Exchange Commission (“SEC”)’s EDGAR site as well as in securitization disclosure documents.
  • If a transaction is rated, issuers are required to file detailed reports of due diligence results with the SEC (Rule 17-Ga2 filings) at least five business days prior to first sale of an offered security. Examples of summary reports for both the Flagstar and JPMorgan Chase securitizations show the additional information on due diligence results provided to investors. For those investors interested in more detail, loan-level reporting of the due diligence findings is also available on EDGAR.

This increased transparency enables investors to independently assess the quality of mortgage loans in a private-label RMBS transaction and factor the results of the due diligence process into their investment decision.

Rating Agency Assessment Process

Over-reliance on rating agencies and the conflict of interest caused by the “issuer pay” model for credit ratings is a frequently cited problem with pre-crisis private-label RMBS transactions. Passage of the Dodd-Frank Act is expected to help reduce the blind reliance by investors and regulators on the ratings process by eliminating the use of credit ratings within the regulatory framework and increasing independent due diligence by investors. Despite tremendous criticism of the “issuer pay” model, the system remains intact almost a decade after the financial crisis across multiple asset classes, including corporate bonds and municipal bonds. The Dodd-Frank Act, however, now requires rating agencies to establish “firewalls” between their business development processes and their ratings processes.

With the criticism levied on the performance and opacity of the rating agency assessment process, the SEC Rule 17g-7 requires public disclosures from rating agencies whenever they provide a credit rating.  With these new disclosures, rating agencies have increased the transparency of the ratings process by making public the following changes to their assessment process:

  • Assumptions, methodologies, and processes used to rate transactions
  • Pre-Sale Reports that outline how a rating agency reviews the specific transaction, including areas such as the capital structure, cash flow triggers, pool characteristics, loan underwriting criteria, representations and warranties, and origination and servicing practices

While many market participants criticize the pre-crisis methodologies used by rating agencies to establish credit enhancement levels, pre-sale reports detail reviews performed on each rated private-label RMBS transaction and the assessments made by rating agencies to compute the expected credit enhancement requirements to support the securitization ratings.

In response to a weak pre-crisis representation and warranty framework (discussed in greater detail in the following section), rating agencies now publish “market standard” representations and warranties for each asset class and compare the representations and warranties in each private-label RMBS transaction being evaluated against the standard. The rating agencies also assess a transaction’s processes for enforcing representations and warranties (including repurchases) when a breach occurs.

Rating agencies typically publish the pre-sale report and their assessment of the representations and warranties a few days before a new private-label RMBS issuance is priced. Together with the preliminary offering documents, these items provide post-crisis PLS market investors a comprehensive view of the transaction’s risk prior to making a pricing / investment decision.

Finally, in another step to reduce the risk of issuers “shopping” for favorable ratings, SEC Rule 17g-5 requires rating agencies to make information provided to them by an issuer available to all other rating agencies. This allows other rating agencies to assess transactions on an equal basis and reach independent conclusions – using the same data – on credit enhancement requirements.

One measure of whether the rating agency process has changed since the crisis is the credit enhancement levels themselves. Higher credit enhancement levels would tend to suggest more stringent ratings. Credit enhancement levels on prime jumbo private-label RMBS can be observed in the tables below.

Post-Crisis Transaction Summary:

Pre-Crisis Transaction Summary:

In general, post-crisis AAA credit enhancement levels are higher today compared to pre-crisis AAA credit enhancement levels, which generally ranged between 3.50% – 4.00%. The rating agency assessment process has become more transparent since the crisis, and credit enhancement levels have increased. The future performance of these transactions will determine whether these changes are sufficient.

Representation and Warranty Framework and Enforcement

Representations and warranties are designed to allocate risks associated with a securitization’s underlying loans between issuers and investors. Basic principles of an effective process for allocating risks associated with underwriting standards, collateral value, or regulatory compliance include:

  • Clear rules (i.e., representations and warranties) defining when loans must be repurchased out of the security
  • Transparent and robust methods for identifying loans that may cause losses
  • Financial stability of the entity responsible for funding required loan repurchases

One criticism of the pre-crisis PLS market was the lack of an independent party tasked with identifying rep and warrant breaches. In many cases, the issuers or sponsors themselves were the only transaction parties capable of conducting the type of forensic loan review necessary to discover breaches. However, because these very parties would be on the hook to fund any repurchases required by their analyses, investors had reason to question the thoroughness of these reviews.

In response, the post-crisis PLS market has generally adapted a rules-based approach that relies on delinquency and other objective “triggers” to review loans and identify potential representation and warranty breaches. Once triggered, reviews are often performed by either 1) an independent third-party with forensic review capabilities, or 2) the holder of the most subordinate outstanding security. Reviews are no longer performed or controlled by issuers whose incentive to identify a breach could be questioned.

These process improvements are meant to increase the likelihood that potential representation and warranty breaches are identified and their terms enforced. If a loan meets the contractual requirements for a repurchase, it is critical that the entity responsible for repurchasing it has the financial ability to do so. New SEC disclosure requirements (Rule 15-Ga1) help track and assess an issuer’s ability to comply with repurchase requests.

Changes in the representation and warranty framework have improved methods for breach identification, evaluation, and enforcement. These changes have increased transparency, clarified the allocation of risk, contractually established roles for identifying and evaluating potential breaches, and brought about more effective enforcement mechanisms.

Loan Quality Standards

The Dodd-Frank Act requires lenders to make a good faith effort to determine borrowers’ ability to repay (ATR) their mortgage obligations. The ATR rule seeks to discourage some of the practices used to originate pre-crisis mortgage loans and requires lenders to consider certain underwriting criteria, such as the borrower’s assets or income, debt load, and credit history, to determine whether a loan can be repaid.

Lenders are presumed to comply with the ATR rule when they originate a “qualified mortgage” (QM) which meets the requirements of the ATR rule and additional underwriting and pricing standards. These requirements generally include a limit on points and fees, along with various restrictions on loan terms and features.2

Risk Retention

The risk retention requirements added by Section 15G of the Securities Exchange Act of 1934 generally require the issuer of securities backed by non-QM loans to retain at least 5 percent of the credit risk of the mortgage loans collateralizing the securities. This rule change helps align the interests of issuers and sponsors with those of investors by requiring issuers and sponsors to retain an economic interest in the credit risk of the assets they securitize. The rule allows issuers and sponsors to retain risk as either a horizontal interest (i.e., retaining the most subordinate 5% of the securitization), a vertical interest (i.e., retaining a “slice” of each security issued), an “L-shaped” interest (i.e., a combination of horizontal and vertical), or a cash reserve account.

For most non-QM securitizations, the issuers and sponsors have migrated towards the vertical interest, which performs like whole loan exposure and avoids the comprehensive fair value disclosures required for retained horizontal interests. At the margin, this change will create “skin in the game” for non-QM issuers and sponsors and better align their incentives with those of investors.

Bondholder Communication

To address concerns expressed by investors in locating other investors to enforce contractual rights, recent private-label RMBS transactions have incorporated mechanisms for investors to communicate with each other. Many transactions have incorporated methods for investors who wish to communicate to be included in a transaction registry, which may allow them to reach the required percentage of security holders necessary to provide specific direction to the trustee.

Summary

The PLS market has experienced a decade of stagnation since the financial crisis of 2007 – 2008. Notwithstanding new entrants to this market, a persistent lack of investor trust in and demand for private-label RMBS remains a challenge. While opportunities for improvement remain, major improvements to the securitization process are beginning to take hold.  These changes in post-crisis private-label RMBS transactions improve transparency, align the incentives of issuers and sponsors with those of investors, and hold the key to attracting investors back to this once-thriving market segment.


[1] Include loans with original term less than 20 years.

[2] Unpermitted features include negative amortization, interest-only payments, loan terms of more than 30 years, and “back-end” debt-to-income ratios above 43%. (The back-end debt-to-income ratio limit does not apply to 1) loans guaranteed by the Federal Housing Administration and Veterans Administration, 2) loans eligible for purchase by Fannie Mae and Freddie Mac, and 3) portfolio loans made by “small creditors.”)


Get Started
Log in

Linkedin   

risktech2024