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Case Study: Securitization Disclosure File Creation Process

The Client

Private Label Mortgage-Backed Security Issuer 

The Problem

The client issues private label MBS with sources from multiple origination channels. In accordance with industry requirements, the client needed to create and make available to securitization counterparties a loan-level data file (the “ASF File”) which has been defined and endorsed by the Structured Finance Industry Group. ​

The process of extraction and aggregation was inefficient and inconsistent with data from various originators, due diligence vendors and service providers.

RiskSpan consulting services streamlined extraction and aggregation, and reconciling the data used in this process.

The Solution

RiskSpan automated and improved the client’s processes to aggregate loan level data and perform data quality business rules. RiskSpan also designed, built, tested, and delivered an automated process to perform quality control business rules and produce the ASF File, while producing a reconciled file meeting ASF File standards and specifications.

Data Lineage

RiskSpan has experience working with various financial institutions on data lineage and its best practices. RiskSpan has also partnered with industry-leading data lineage solution providers to harness technical solutions for data lineage.

Data Quality

It’s increasingly important to reduce inefficiency in the data process and one of the key criteria to achieve the same is to ensure Data is of highest quality for downstream or any other analytical application usage. Riskspan experience in data quality stems from working with raw loan and transactional data from some of the world’s largest financial institutions.

The Deliverables

  • Created and documented data dictionary, data mapping, business procedures and business flows​
  • Gathered criteria and knowledge, from various client departments, to assess the reasonableness of data used in the securitization process ​
  • Documented client-specific business logic and business rules to reduce resource dependency and increase organizational transparency​
  • Enforced business rules through an automated mechanism, reducing manual effort and data scrub process time​
  • Delivered exception reporting which enabled the client to track, measure and report inaccuracies in data from due diligence firm​
  • Eliminated maintenance and dependency on ad hoc data sources and manual work-arounds​

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.

Footnotes

1 Kiff, John. “Back to Basics: What is LIBOR?” International Monetary Fund. Accessed November 2018. December 2012. https://www.imf.org/external/pubs/ft/fandd/2012/12/basics.htm, Accessed November 2018.

“LIBOR – current LIBOR interest rates.” Global Rates. https://www.global-rates.com/interest-rates/libor/libor.aspx, Accessed November 2018.

Bailey, Andrew. “The Future of LIBOR.” Financial Conduct Authority. 27 July 2017. https://www.fca.org.uk/news/speeches/the-future-of-libor, 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. https://www.justice.gov/opa/pr/two-former-deutsche-bank-traders-convicted-role-scheme-manipulate-critical-global-benchmark, Accessed November 2018.

Bailey, Andrew. “The Future of LIBOR.” Financial Conduct Authority. 27 July 2017. https://www.fca.org.uk/news/speeches/the-future-of-libor, Accessed November 2018.

6 Alternative Reference Rates Committee. “Second Report.” Federal Reserve Bank of New York. March 2018. https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2018/ARRC-Second-report, Accessed November 2018.

Alternative Reference Rates Committee. Federal Reserve Bank of New York. https://www.newyorkfed.org/arrc/index.html, Accessed November 2018.


Asset Manager: Cost-Efficient and Flexible Solution

An asset management company needed to replace an inflexible risk system provided by a Wall Street dealer.  The client’s portfolio was diverse, with a sizable concentration in structured securities and mortgage assets. The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.

Every portfolio manager requires reliable and accurate analytics to manage risk and improve investment decisions. They require understanding of investment positions and the impacts on risk metrics measures such as value at risk (VaR). The faster they can assess a portfolio’s total exposure and understand the key drivers, the better they can react and align activities with the overall firm risk appetite.

“The challenge was that our existing daily process for calculating, validating and reporting market and credit risk metrics required significant manual work. If we could get to the answers faster, we would be in a much better position to identify exposures and address potential problems.”                             

The Solution

As a fully-managed solution, RiskSpan’s Edge Platform provides the asset manager with a cost-efficient and flexible solution. The service bundles required data feeds, infrastructure management, and predictive models for mortgages and structured products. Edge manages and validates third-party data as well as client portfolio data, and produces scenario analytics in a secure hosted environment. With the combination of models, data management, and an end-to-end managed process, Edge provides the asset manager with unmatched value.

The Benefits

  • Portfolio risk measures on-demand
  • Structured product expertise
  • Outsourced data management
  • Predictive models for mortgages
  • Outsourced hardware management
  • Customized dashboards and reports

The asset manager used the Edge Platform to cut hours from daily risk-reporting processes and free several analysts to focus on their primary task: optimizing analytics and processes that support better investment decisions.

Deliverables

Analytics Software

The Edge Platform provides for the calculation of key market risk metrics for over 70 different instrument types. The service provides for a combination of on-demand or overnight batch processing. Users have online access to platform to run ad-hoc analyses, including additional scenarios or what-if analyses. The hosted platform makes the processing speed lightning fast.

Data Management Outsourced

The Edge market-risk analytics platform integrates data from six major data vendors.  Our data management services support integrated data for interest-rates, implied volatility, and terms & conditions for over 70 different instrument types. The platform includes loan-level data for Agency and non-Agency mortgage-backed products. The platform integrates seamlessly with Intex subroutines to support all structured products.  Further, Edge clients have access to a team of experts in mortgage and structured product – not just technical support.

Technology and Infrastructure Management

As a hosted solution, the asset manager is able to leave management of hardware to the Edge technology team. We secure and manage all required hardware, freeing up millions of dollars in hardware acquisition costs and labor costs required to manage the infrastructure.


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.

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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.


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.


CRT Exposure to Hurricane Michael

Graph

With Hurricane Michael approaching the Gulf Coast, we put together some interactive charts looking at the affected metro areas, and their related CRT exposure (Both CAS and STACR). Given the large area of impact with Hurricane Michael, we have included a nearly exhaustive selection of MSA’s. Click on a deal ID along the left-hand side of the plot to view its exposure to each MSA. Most of the mortgage delinquencies in the wake of Hurricane Harvey quickly cured. Holders of securities backed by loans that ultimately defaulted (typically because the property was completely destroyed) had much of their exposure mitigated by insurance proceeds, government intervention, and other relief provisions.  






Analytics-as-a-Service – CECL Forecasting

The RiskSpan Edge Platform CECL Module delivers the technology platform and expertise to take you from where you are today to producing audit-ready CECL estimates. Our dedicated CECL Module executes your monthly loss reserving and reporting process under the new CECL standard, covering data intake, segmentation, modeling, and report generation within a single platform. Watch RiskSpan Director David Andrukonis explain the Edge CECL Module in this video.

 

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RiskSpan Edge Platform API

The RiskSpan Edge Platform API enables direct access to all data from the RS Edge Platform. This includes both aggregate analytics and loan-and pool-level data.  Standard licensed users may build queries in our browser-based graphical interface. But, our API is a channel for power users with programming skills (Python, R, even Excel) and production systems that are incorporating RS Edge Platform components as part of their Service Oriented Architecture (SOA).

Watch RiskSpan Director LC Yarnelle explain the Edge API in this video!

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CRT Deal Monitor: Understanding When Credit Becomes Risky

This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis. You’ll see how credit metrics are trending today and understand the significance of today’s shifts in the context of historical data. Some of the charts in this post have interactive features, so click around! We’ll be tweaking the analysis and adding new metrics in subsequent months. Please shoot us an email if you have an idea for other metrics you’d like us to track.

Highlights

  • Performance metrics signal steadily increasing credit risk, but no cause for alarm.
    • We’re starting to see the hurricane-related (2017 Harvey and Irma) delinquency spikes subside in the deal data. Investors should expect a similar trend in 2019 due to Hurricane Florence.
    • The overall percentage of delinquent loans is increasing steadily due to the natural age ramp of delinquency rates and the ramp-up of the program over the last 5 years.
    • Overall delinquency levels are still far lower than historical rates.
    • While the share of delinquency is increasing, loans that go delinquent are ending up in default at a lower rate than before.
  • Deal Profiles are becoming riskier as new GSE acquisitions include higher-DTI business.
    • It’s no secret that both GSEs started acquiring a lot of high-DTI loans (for Fannie this moved from around 16% of MBS issuance in Q2 2017 to 30% of issuance as of Q2 this year). We’re starting to see a shift in CRT deal profiles as these loans are making their way into CRT issuance.
    • The credit profile chart toward the end of this post compares the credit profiles of recently issued deals with those of the most recent three months of MBS issuance data to give you a sense of the deal profiles we’re likely to see over the next 3 to 9 months. We also compare these recently issued deals to a similar cohort from 2006 to give some perspective on how much the credit profile has improved since the housing crisis.
    • RiskSpan’s Vintage Quality Index reflects an overall loosening of credit standards–reminiscent of 2003 levels–driven by this increase in high-DTI originations.
  • Fannie and Freddie have fundamental differences in their data disclosures for CAS and STACR.
    • Delinquency rates and loan performance all appear slightly worse for Fannie Mae in both the deal and historical data.
    • Obvious differences in reporting (e.g., STACR reporting a delinquent status in a terminal month) have been corrected in this analysis, but some less obvious differences in reporting between the GSEs may persist.
    • We suspect there is something fundamentally different about how Freddie Mac reports delinquency status—perhaps related to cleaning servicing reporting errors, cleaning hurricane delinquencies, or the way servicing transfers are handled in the data. We are continuing our research on this front and hope to follow up with another post to explain these anomalies.

The exceptionally low rate of delinquency, default, and loss among CRT deals at the moment makes analyzing their credit-risk characteristics relatively boring. Loans in any newly issued deal have already seen between 6 and 12 months of home price growth, and so if the economy remains steady for the first 6 to 12 months after issuance, then that deal is pretty much in the clear from a risk perspective. The danger comes if home prices drift downward right after deal issuance. Our aim with this analysis is to signal when a shift may be occurring in the credit risk inherent in CRT deals. Many data points related to the overall economy and home prices are available to investors seeking to answer this question. This analysis focuses on what the Agency CRT data—both the deal data and the historical performance datasets—can tell us about the health of the housing market and the potential risks associated with the next deals that are issued.

Current Performance and Credit Metrics

Delinquency Trends

The simplest metric we track is the share of loans across all deals that is 60+ days past due (DPD). The charts below compare STACR (Freddie) vs. CAS (Fannie), with separate charts for high-LTV deals (G2 for CAS and HQA for STACR) vs. low-LTV deals (G1 for CAS and DNA for STACR). Both time series show a steadily increasing share of delinquent loans. This slight upward trend is related to the natural aging curve of delinquency and the ramp-up of the CRT program. Both time series show a significant spike in delinquency around January of this year due to the 2017 hurricane season. Most of these delinquent loans are expected to eventually cure or prepay. For comparative purposes, we include a historical time series of the share of loans 60+ DPD for each LTV group. These charts are derived from the Fannie Mae and Freddie Mac loan-level performance datasets. Comparatively, today’s deal performance is much better than even the pre-2006 era. You’ll note the systematically higher delinquency rates of CAS deals. We suspect this is due to reporting differences rather than actual differences in deal performance. We’ll continue to investigate and report back on our findings.

Delinquency Outcome Monitoring

While delinquency rates might be trending up, loans that are rolling to 60-DPD are ultimately defaulting at lower and lower rates. The tables below track the status of loans that were 60+ DPD. Each bar in the chart represents the population of loans that were 60+ DPD exactly 6 months prior to the x-axis date. Over time, we see growing 60-DPD and 60+ DPD groups, and a shrinking Default group. This indicates that a majority of delinquent loans wind up curing or prepaying, rather than proceeding to default. The choppiness and high default rates in the first few observations of the data are related to the very low counts of delinquent loans as the CRT program ramped up. The following table repeats the 60-DPD delinquency analysis for the Freddie Mac Loan Level Performance dataset leading up to and following the housing crisis. (The Fannie Mae loan level performance set yields a nearly identical chart.) Note how many more loans in these cohorts remained delinquent (rather than curing or defaulting) relative to the more recent CRT loans. https://plot.ly/~dataprep/30.embed

Vintage Quality Index

RiskSpan’s Vintage Quality Index (VQI) reflects a reversion to the looser underwriting standards of the early 2000s as a result of the GSEs’ expansion of high-DTI lending. RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. We use the metric as an empirically grounded way to control for vintage differences within our credit model. VQI-History While both GSEs increased high-DTI lending in 2017, it’s worth noting that Fannie Mae saw a relatively larger surge in loans with DTIs greater than 43%. The chart below shows the share of loans backing MBS with DTI > 43. We use the loan-level MBS issuance data to track what’s being originated and acquired by the GSEs because it is the timeliest data source available. CRT deals are issued with loans that are between 6 and 20 months seasoned, and so tracking MBS issuance provides a preview of what will end up in the next cohort of deals. High DTI Share

Deal Profile Comparison

The tables below compare the credit profiles of recently issued deals. We focus on the key drivers of credit risk, highlighting the comparatively riskier features of a deal. Each table separates the high-LTV (80%+) deals from the low-LTV deals (60%-80%). We add two additional columns for comparison purposes. The first is the ‘Coming Cohort,’ which is meant to give an indication of what upcoming deal profiles will look like. The data in this column is derived from the most recent three months of MBS issuance loan-level data, controlling for the LTV group. These are newly originated and acquired by the GSEs—considering that CRT deals are generally issued with an average loan age between 6 and 15 months, these are the loans that will most likely wind up in future CRT transactions. The second comparison cohort consists of 2006 originations in the historical performance datasets (Fannie and Freddie combined), controlling for the LTV group. We supply this comparison as context for the level of risk that was associated with one of the worst-performing cohorts. The latest CAS deals—both high- and low-LTV—show the impact of increased >43% DTI loan acquisitions. Until recently, STACR deals typically had a higher share of high-DTI loans, but the latest CAS deals have surpassed STACR in this measure, with nearly 30% of their loans having DTI ratios in excess of 43%. CAS high-LTV deals carry more risk in LTV metrics, such as the percentage of loans with a CLTV > 90 or CLTV > 95. However, STACR includes a greater share of loans with a less-than-standard level of mortgage insurance, which would provide less loss protection to investors in the event of a default. Credit Profile Low-LTV deals generally appear more evenly matched in terms of risk factors when comparing STACR and CAS. STACR does display the same DTI imbalance as seen in the high-LTV deals, but that may change as the high-DTI group makes its way into deals. Low-LTV-Deal-Credit-Profile-Most-Recent-Deals

Deal Tracking Reports

Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for August, CDR numbers are not provided because they are not fully populated yet. Fannie Mae CAS default data is delayed an additional month relative to STACR. We’ve left loss and severity metrics blank for fixed-loss deals. STACR-Deals-over-the-past-3-months CAS-Deals-from-the-past-3-months.

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