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Top Hedge Fund Administrator: Risk Metrics & Performance Reports via Tableau and the Cloud​

A leading hedge fund administrator sought a better way to provide compliance reporting and overnight risk and portfolio reporting for its clients.

Reporting at this scale requires extraordinarily flexibility in computational bandwidth.

The Solution

RiskSpan delivered computation and distribution via the cloud of all required analytics and risk metrics to all relevant parties using the flexibility and attractive visualization of a seamless Tableau integration.

  • Ingestion, validation, and integration of disparate data sources (rates, implied volatility data and terms and conditions from six data vendor sources)
  • Reporting, distribution and publishing of the client’s full range of risk metrics, including VaR, custom aggregation, scenario analyses, interest rate shocks and other stress testing — all readily viewable to every client stakeholder via the cloud using Tableau.

The Edge We Provided

A fully hosted, outsourced solution. The administrator’s highly dynamic reports are delivered by way of a secure, hosted environment to a large number of diverse, institutional clients.


December 2 Workshop: Structured Data Extraction from Image with Google Document AI

Recorded: Dec. 2nd | 1:00 p.m. EDT

RiskSpan Director Steven Sun shares a procedural approach to tackling the difficulties of efficiently extracting structured data from images, scanned documents, and handwritten documents using Google’s latest Document AI Solution. This approach greatly improves:

  • Effectiveness and accuracy of extracting data which will be otherwise difficult or impossible, and 
  • Automating and streamlining the process of feeding extracted data into a data analytic framework

Steven Sun

Director, RiskSpan


Chart of the Month: Fed Impact on Credit ETF Performance

On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase. 

In June, we charted the impact of this announcement on the credit spreads of various corporate bonds. This month we are charting its impact on ETF performance.

This month’s chart plots the price of ETFs relative to their price as of March 23rd 2020 (i.e., all ETF prices are set to 1.00 as of that date). Data runs from Feb 24th to Nov 16th 2020.


RiskSpan’s EDGE Platform Named Risk-as-a-Service Category Winner by Chartis Research

ARLINGTON, Va., November 19, 2020 – RiskSpan’s EDGE Platform has been named the “Risk-as-a-Service” Category Winner for 2021 in Chartis Research’s prestigious RiskTech100 Rankings. In winning the RaaS category, RiskSpan edged out strong competing offerings from many other long-established risk service providers.

Chartis_RiskTech100-2021The award caps a successful year for RiskSpan and the EDGE Platform, which underwent a number of key enhancements and, notwithstanding the pandemic, has experienced a 29 percent increase in its subscriber base since the start of 2020.

 

EDGE is an end-to-end, cloud-native platform leveraging mortgage and other structured finance data, modeling, and application layers to allow information to seamlessly flow across products while being securely accessible anytime and anywhere. The Risk Service’s data, insights, and reports are available via the website, integrated via an API, or ingested and distributed as part of a fully managed service.

“The Risk-as-a-Service award was especially competitive this year with many strong offerings,” observed Mark Feeley, Chartis Global Brand Director. “The RiskSpan EDGE solution’s ability to scale and deliver via the cloud is reflected in its category win.”

“We are honored to receive this recognition,” noted Bernadette Kogler, RiskSpan’s co-founder and CEO. “I am proud of our design and development teams who have worked tirelessly through challenging circumstances to continuously enhance EDGE’s Risk component for our expanding community of client users.”

About RiskSpan

RiskSpan offers end-to-end solutions for data management, risk management analytics, and visualization on a highly secure, fast, and fully scalable platform that has earned the trust of the industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s EDGE platform integrates a range of data-sets–including both structured and unstructured–and off-the-shelf analytical tools to provide you with powerful insights and a competitive advantage. Learn more at www.riskspan.com.

Media Contact

Timothy Willis
Email: twillis@riskspan.com


Executive Interview: Inside the OCC

Watch RiskSpan CEO Bernadette Kogler’s interview with Acting Comptroller of the Currency Brian Brooks.

They discuss many topics include the OCC’s Project REACh, machine learning models to expand the credit box, blockchain’s role in housing finance, and the expanding definition of a chartered institution.

WATCH THE INTERVIEWRead More


Workshop: Measuring and Visualizing Feature Impact & Machine Learning Model Materiality

Recorded: Oct. 28th | 1:00 p.m. EDT

RiskSpan CIO Suhrud Dagli, who discussed how ML is being incorporated into model risk management during our Sep. 30 webinar: Machine Learning in Model Validation, demonstrates in greater detail how machine learning can be used:

  • In input data validations,
  • To measure feature impact, and
  • To visualize how multiple features interact with each other

Suhrud Dagli


Co-Founder & Fintech Lead, RiskSpan


Why Model Validators Need to Care About the LIBOR Transition

The transition to the Secured Overnight Financing Rate (SOFR) as a LIBOR replacement after 2021 creates layers of risk for banks. Many of these risks are readily apparent, others less so. But the factors banks must consider while choosing replacement rates and correctly implementing contractual fallback language makes a seamless transition a daunting proposition. Though sometimes overlooked, model risk managers have an important role in ensuring this happens correctly and in a way that does not jeopardize the reliability of model outputs.   

LIBOR, SOFR and the need for transition

A quick refresher: The London Interbank Offered Rate (LIBOR) currently serves as the benchmark at which major global banks lend to one another on a short-term basis in the international interbank market. LIBOR is calculated by the Intercontinental Exchange (ICE) and is published daily. LIBOR is based on a combination of five currencies and seven maturities. The most common of these is the three-month U.S. Dollar rate.

Accusations of manipulation by major banks going back as early as 2008, however, raised concerns about the sustainability of LIBOR. A committee convened by the Federal Reserve Board and the Federal Reserve Bank of New York in 2017—the Alternative Reference Rates Committee (ARRC)—identified a broad Treasury repurchase agreement (repo) financing rate as its preferred alternative reference rate to replace LIBOR after 2021. This repo rate (now known as SOFR) was chosen for its ability to provide liquidity to underlying markets and because the volumes underlying SOFR are far larger than any other U.S. money market. This combination of size and liquidity contributes to SOFR’s transparency and protects market participants from attempts at manipulation.

What Does This Mean for MRM?

Because the transition has potential bearing on so many layers of risk—market risk, operational risk, strategic risk, reputation risk, compliance risk, not to mention the myriad risks associated with mispricing assets—any model in a bank’s existing inventory that is tasked with gauging or remediating these risks is liable to be impacted. Understanding how and the extent to which models are considering how LIBOR transition may affect pricing and other core processes are (or should be) of principal concern to model validators.

Ongoing Monitoring and Benchmarking

Regulatory guidance and model validation best practices require testing model inputs and benchmarking how the model performs with the selected inputs relative to alternatives. For this reason, the validation any model whose outputs are sensitive to variable interest rates should include an assessment of how a replacement index (such as SOFR) and adjustment methodology were selected.

Model validators should be able to ascertain whether the model developer has documented enough evidence relating to:

  • Available reference rates and the appropriateness of each to the bank’s specific products
  • System capabilities for using these replacement rates with the bank’s products.
  • Control risks associated with unavailable alternative rates


Fallback Language considerations:

Fallback language—contractual provisions that govern the process for selecting a replacement rate in the event of LIBOR termination—should also factor into a validator’s assessment of model inputs. While many existing fallback provisions can be frustratingly vague when it comes to dealing with a permanent cessation of LIBOR, validators of models that rely on reference rates as inputs have an obligation to determining compliance with fallback language containing clear and executable terms. These include:

  • Specific triggers to enact the replacement rate
  • Clarity regarding the replacement rate and spread adjustments
  • Permissible options under fallback language – and whether other options might be more appropriate than the one ultimately selected based on the potential for valuation changes, liquidity impact, hedging implications, system changes needed, and customer impact

In November 2019, the ARRC published the finalized fallback language for residential adjustable rate mortgages, bilateral business loans, floating rate notes, securitizations, and syndicated loans. It has also actively engaged with the International Swap Derivatives Association (ISDA) to finalize the fallback parameters for derivatives.

The ARRC also recommended benchmark replacement rates adjusted for spread that would replace the current benchmark due to circumstances that trigger the replacement. The recommendation included the following benchmark replacement waterfalls. Validators of models relying on these replacements may choose, as part of their best practices review, to determine the extent to which existing fallback provisions align with the recommendations.

Replacement Description
Term SOFR + spread adjustment Forward-looking term SOFR for the applicable corresponding tenor. Note: Loan recommendations allow use of the next longest tenor term SOFR rate if the corresponding tenor is unavailable  
Compounded SOFR + spread Adjustment Compounded average of daily SOFRs over the relevant period depending on the tenor of USD LIBOR being replaced
Relevant selected rate + spread adjustment   Rate selected by the Relevant Governmental Body, lender, or borrower & administrative agent
Relevant ISDA replacement rate + spread adjustment The applicable replacement rate (without spread adjustment) that is embedded in ISDA’s standard definitions  
Issuer, designated transaction representative or noteholder replacement + spread adjustment An identified party will select a replacement rate, in some cases considering any industry-accepted rate in the related market. Note: in certain circumstances this step could be omitted


Model risk managers can sometimes be lulled into believing that the validation of interest rate inputs consists solely of verifying their source and confirming that they have been faithfully brought into the model. Ultimately, however, model validators are responsible for verifying not only the provenance of model inputs but also their appropriateness. Consequently, ensuring a smooth transition to the most appropriate available reference rate replacement is of paramount importance to risk management efforts related to the models these rates feed.


RESOURCES:

https://www.insideafricalaw.com/blog/benchmark-reform-the-impact-of-libor-transition-on-the-african-project-finance-market

https://www.occ.treas.gov/news-issuances/bulletins/2020/bulletin-2020-68.html

https://www.isda.org/a/n6tME/Supplemental-Consultation-on-USD-LIBOR-CDOR-HIBOR-and-SOR.pdf

https://www.investopedia.com/terms/l/libor.asp

https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2020/ARRC-factsheet.pdf

https://www.newyorkfed.org/arrc/sofr-transition

https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2019/LIBOR_Fallback_Language_Summary

https://www.isda.org/a/n6tME/Supplemental-Consultation-on-USD-LIBOR-CDOR-HIBOR-and-SOR.pdf

http://assets.isda.org/media/50b3fed0/47be9435-pdf/


The information within this section has been taken directly from the https://www.occ.treas.gov/news-issuances/bulletins/2020/bulletin-2020-68.html [AR1]


September 17 Webinar: Using Alternative Data to Widen the Credit Box

Recorded:
Sep. 17th | 1:00 p.m. EDT

RiskSpan’s Bernadette Kogler led a panel of industry experts in a review of the U.S. economy and how mortgage companies can employ alternative data to responsibly extend mortgage credit more broadly to current and potential homeowners.

Participants include 

  • Bernadette Kogler, Co-Founder & CEO, RiskSpan
  • Amy Crews Cutts, President, AC Cutts and Associates
  • Janet Jozwik, Managing Director, RiskSpan
  • Laurie Goodman, Director, Housing Finance Policy Center, The Urban Institute

GET RECORDING


September 30 Webinar: Machine Learning in Model Validation

Recorded: September 30th | 1:00 p.m. EDT

Join our panel of experts as they share their latest work using machine learning to identify and validate model inputs.

  • Suhrud Dagli, Co-Founder & Fintech Lead, RiskSpan
  • Jacob Kosoff, Head of Model Risk Management & Validation, Regions Bank
  • Nick Young, Head of Model Validation, RiskSpan
  • Sanjukta Dhar, Consulting Partner, Risk and Regulatory Compliance Strategic Initiative, TCS Canada


Featured Speakers

Suhrud-Dagli

Suhrud Dagli

Co-Founder & Fintech Lead, RiskSpan

Jacob Kosoff

Head of Model Risk Management & Validation, Regions Bank

dan-kim

Nick Young

Head of Model Validation, RiskSpan

Sanjukta Dhar

Sanjukta Dhar

Consulting Partner, Risk and Regulatory Compliance Strategic Initiative, Tata Consulting


Consistent & Transparent Forbearance Reporting Needed in the PLS Market

There is justified concern within the investor community regarding the residential mortgage loans currently in forbearance and their ultimate resolution. Although most of the 4M loans in forbearance are in securities backed by the Federal Government (Fannie Mae, Freddie Mac or Ginnie Mae), approximately 400,000 loans currently in forbearance represent collateral that backs private-label residential mortgage-backed securities (PLS). The PLS market operates without clear, articulated standards for forbearance programs and lacks the reporting practices that exist in Agency markets. This leads to disparate practices for granting forbearance to borrowers and a broad range of investor reporting by different servicers. COVID-19 has highlighted the need for transparent, consistent reporting of forbearance data to investors to support a more efficient PLS market.

Inconsistent investor reporting leaves too much for interpretation. It creates investor angst while making it harder to understand the credit risk associated with underlying mortgage loans. RiskSpan performed an analysis of 2,542 PLS deals (U.S. only) for which loan-level foreclosure metrics are available. The data shows that approximately 78% of loans reported to be in forbearance were backing deals originated between 2005-2008 (“Legacy Bonds”).  As you would expect, new issue PLS has a smaller percentage of loans reported to be in forbearance.

% total forebearance UPB

Not all loans in forbearance will perform the same and it is critical for investors to receive transparent reporting of underlying collateral within their PLS portfolio in forbearance.  These are unchartered times and, unlike historic observations of borrowers requesting forbearance, many loans presently in forbearance are still current on their mortgage payments. In these cases, they have elected to join a forbearance program in case they need it at some future point. Improved forbearance reporting will help investors better understand if borrowers will eventually need to defer payments, modify loan terms, or default leading to foreclosure or sale of the property.

In practice, servicers have followed GSE guidance when conducting forbearance reviews and approval. However, without specific guidance, servicers are working with inconsistent policies and procedures developed on a company-by-company basis to support the COVID forbearance process. For example, borrowers can be forborne for 12-months according to FHFA guidance. Some servicers have elected to take a more conservative approach and are providing forbearance in 3-month increments with extensions possible once a borrower confirms they remain financially impacted by the COVID pandemic.

Servicers have the data that investors want to analyze. Inconsistent practices in the reporting of COVID forbearances by servicers and trustees has resulted in forbearance data being unavailable on certain transactions. This means investors are not able to get a clear picture of the financial health of borrowers in transactions. In some cases, trustees are not reporting forbearance information to investors which makes it nearly impossible to obtain a reliable credit assessment of the underlying collateral.  

The PLS market has attempted to identify best practices for monthly loan-level reporting to properly assess the risk of loans where forbearance has been granted.  Unfortunately, the current market crisis has highlighted that not all market participants have adopted the best practices and there are not clear advantages for issuers and servicers to provide clear, transparent forbearance reporting. At a minimum, RiskSpan recommends that the following forbearance data elements be reported by servicers for PLS transactions:

  • Last Payment Date: The last contractual payment date for a loan (i.e. the loan’s “paid- through date”).
  • Loss Mitigation Type: A code indicating the type of loss mitigation the servicer is pursuing with the borrower, loan, or property.
  • Forbearance Plan Start Date: The start date when either a) no payment or b) a payment amount less than the contractual obligation has been granted to the borrower.
  • Forbearance Plan Scheduled End Date: The date on which a Forbearance Plan is scheduled to end.
  • Forbearance Exit – Reason Code: The reason provided by the borrower for exiting a forbearance plan.
  • Forbearance Extension Requested: Flag indicating the borrower has requested one or more forbearance extensions.
  • Repayment Plan Start Date: The start date for when a borrower has agreed to make monthly mortgage payments greater than the contractual installment in an effort to repay amounts due during a Forbearance Plan.
  • Repayment Plan Scheduled End Date: The date at which a Repayment Plan is scheduled to end.
  • Repayment Plan Violation Date: The date when the borrower ceased complying with the terms of a defined repayment plan.

The COVID pandemic has highlighted monthly reporting weaknesses by servicers in PLS transactions. Based on investor discussions, additional information is needed to accurately assess the financial health of the underlying collateral. Market participants should take the lessons learned from the current crisis to re-examine prior attempts to define monthly reporting best practices. This includes working with industry groups and regulators to implement consistent, transparent reporting policies and procedures that provide investors with improved forbearance data.


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