Automate Your Data Normalization and Validation Processes
Robotic Process Automation (RPA) is the solution for automating mundane, business-rule based processes so that organizations high value business users can be deployed to more valuable work.
McKinsey defines RPA as “software that performs redundant tasks on a timed basis and ensures that they are completed quickly, efficiently, and without error.” RPA has enormous savings potential. In RiskSpan’s experience, RPA reduces staff time spent on the target-state process by an average of 95 percent. On recent projects, RiskSpan RPA clients on average saved more than 500 staff hours per year through simple automation. That calculation does not include the potential additional savings gained from the improved accuracy of source data and downstream data-driven processes, which greatly reduces the need for rework.
The tedious, error-ridden, and time-consuming process of data normalization is familiar to almost all organizations. Complex data systems and downstream analytics are ubiquitous in today’s workplace. Staff that are tasked with data onboarding must verify that source data is complete and mappable to the target system. For example, they might ensure that original balance is expressed as dollar currency figures or that interest rates are expressed as percentages with three decimal places.
Effective data visualizations sometimes require additional steps, such as adding calculated columns or resorting data according to custom criteria. Staff must match the data formatting requirements with the requirements of the analytics engine and verify that the normalization allows the engine to interact with the dataset. When completed manually, all of these steps are susceptible to human error or oversight. This often results in a need for rework downstream and even more staff hours.
Recently, a client with a proprietary datastore approached RiskSpan with the challenge of normalizing and integrating irregular datasets to comply with their data engine. The non-standard original format and the size of the data made normalization difficult and time consuming.
After ensuring that the normalization process was optimized for automation, RiskSpan set to work automating data normalization and validation. Expert data consultants automated the process of restructuring data in the required format so that it could be easily ingested by the proprietary engine.
Our consultants built an automated process that normalized and merged disparate datasets, compared internal and external datasets, and added calculated columns to the data. The processed dataset was more than 100 million loans, and more than 4 billion records. To optimize for speed, our team programmed a highly resilient validation process that included automated validation checks, error logging (for client staff review) and data correction routines for post-processing and post-validation.
This custom solution reduced time spent onboarding data from one month of staff work down to two days of staff work. The end result is a fully–functional, normalized dataset that can be trusted for use with downstream applications.
RiskSpan’s experience automating routine business processes reduced redundancies, eliminated errors, and saved staff time. This solution reduced resources wasted on rework and its associated operational risk and key-person dependencies. Routine tasks were automated with customized validations. This customization effectively eliminated the need for staff intervention until certain error thresholds were breached. The client determined and set these thresholds during the design process.
RiskSpan data and analytics consultants are experienced in helping clients develop robotic process automation solutions for normalizing and aggregating data, creating routine, reliable data outputs, executing business rules, and automating quality control testing. Automating these processes addresses a wide range of business challenges and is particularly useful in routine reporting and analysis.
Talk to RiskSpan today about how custom solutions in robotic process automation can save time and money in your organization.
Robotic Process Automation – Warehouse Line Reporting
Robotic Process Automation (RPA) is the solution for automating mundane, business-rule based processes so that your high value business users can be deployed to more valuable work.
McKinsey defines RPA as “software that performs redundant tasks on a timed basis and ensures that they are completed quickly, efficiently, and without error.” RPA has enormous savings potential. In RiskSpan’s experience, RPA reduces staff time spent on the target-state process by an average of 95 percent. On recent projects, RiskSpan RPA clients on average saved more than 500 staff hours per year through simple automation. That calculation does not include the potential additional savings gained from the improved accuracy of source data and downstream data-driven processes, which greatly reduces the need for rework.
Managing warehouse lines of credit pose a unique set of challenges to both lending and borrowing institutions. These lines revolve based on frequent, periodic transactions. The loan-level data underlying these transactions, while similar from one transaction to the next, are sufficiently nuanced to require individual review. These reviews are painstaking and can take an inordinate amount of time.
Recently, a consumer financing provider approached RiskSpan with the challenge of tracking its requests to a warehouse lender, so that it could better manage its warehouse loan portfolio. This client had a series of manual reporting processes that it ran upon each request to the warehouse lender to inform oversight of its portfolio. It needed assistance improving the accuracy and resource burden required to produce the reports.
RiskSpan responded to the challenge by completing a rapid RPA readiness assessment and by implementing automation to solve for the data challenges it uncovered. In the readiness assessment, RiskSpan deployed a consultant to ensure that the existing reports were enough to meet the needs of the organization; that source data was enough for the desired reporting; and that data transformation processes (people and systems) were maintaining data quality from input to output.
Once these processes were analyzed and a target-state was confirmed, RiskSpan consultants quickly got to work. We automated ingestion of data for two of the existing reports, automated high-value parts of the data normalization processes and created automated quality control tests for each report.
This custom solution reduced the cycle time from one hour of staff work to 5 minutes of staff work at each warehouse lender request. This saved more than two full weeks of staff time over the course of the year and dramatically increased the scalability of this valuable process.
RiskSpan’s experience automating routine business processes reduced redundancies, eliminated errors, and saved staff time. Our solution reduced resources wasted on rework and its associated operational risk and key-person dependencies. Routine tasks were automated with customized validations. This customization effectively eliminated the need for staff intervention until certain error thresholds were breached. The client determined and set these thresholds during the design process.
RiskSpan data and analytics consultants are experienced in helping clients develop robotic process automation solutions for normalizing and aggregating data, creating routine, reliable data outputs, executing business rules, and automating quality control testing. Automating these processes addresses a wide range of business challenges and is particularly useful in routine reporting and analysis.
Talk to RiskSpan today about how custom solutions in robotic process automation can save time and money in your organization.
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. ______________________________________________________________________________________________
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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SOFR, So Good? The Main Anxieties Around the LIBOR Transition
SOFR Replacing LIBOR
The London Interbank Offered Rate (LIBOR) is going away, and the international financial community is working hard to plan for and mitigate risks to make a smooth transition. In the United States, the Federal Reserve’s Alternative Reference Rates Committee (ARRC) has recommended the Secured Overnight Financing Rate (SOFR) as the preferred replacement rate. The New York Fed began publishing SOFR regularly on April 3, 2018. In July 2018, Fannie Mae issued $6 billion in SOFR-denominated securities, leading the way for other institutions who have since followed suit. In November 2018, the Federal Home Loan (FHL) Banks issued $4 billion in debt tied to SOFR. CME Group, a derivatives and futures exchange company, launched 3-month and 1-month SOFR futures contracts in 2018. All of these steps to support liquidity and demonstrate SOFR demand are designed to create a rate more robust than LIBOR—the transaction volume underpinning SOFR rates is around $750 billon daily, compared to USD LIBOR’s estimated $500 million in daily transaction volume.
USD LIBOR is referenced in an estimated $200 trillion of financial contracts, of which 95 percent is derivatives. However, the remaining cash market is not small. USD LIBOR is referenced in an estimated: $3.4 trillion in business loans, $1.3 trillion in retail mortgages and other consumer loans, $1.8 trillion in floating rate debt, and $1.8 trillion in securitized products.
The ARRC has held consultations on its recommended fallback language for floating rate notes and syndicated business loans—the responses are viewable on the ARRC website. On December 7, the ARRC published consultations on securitizations and bilateral business loans, which are both open for comment through February 5, 2019.
Amid the flurry of positive momentum in the transition towards SOFR, anxiety remains that the broader market is not moving quickly enough. ARRC consultations and working groups indicate that these anxieties derive primarily from a few specific points of debate: development of term rates, consistency of contracts, and implementation timing.
Term Rates
Because the SOFR futures market remains immature, term rates cannot be developed without significant market engagement with the newly created futures. The ARRC Paced Transition Plan includes a goal to create a forward-looking reference rate by end-of-year 2021 – just as LIBOR is scheduled to phase out. In the interim, financial institutions must figure out how to build into existing contracts fallback language or amendments that include a viable alternative to LIBOR term rates.
The nascent SOFR futures market is growing quickly, with December 2018 daily trade volumes at nearly 16,000. However, they pale in comparison to Eurodollar futures volumes, which logged daily averages around 5 million per day at CME Group alone. This puts SOFR on track according to the ARRC plan, but means institutions remain in limbo until the futures market is more mature and term SOFR rates can be developed.
In July 2018, the Financial Stability Board (FSB) stated their support for employment of term rates primarily in cash markets, while arguing that spreads are tightest in derivative markets focused around overnight risk-free rates (RFRs), which therefore are preferred. An International Swaps and Derivatives Association (ISDA) FAQ document published in September 2018 explained the FSB’s request that “ISDA should develop fallbacks that could be used in the absence of suitable term rates and, in doing so, should focus on calculations based on the overnight RFRs.” This marks a major change, given that derivatives commonly reference 3-month LIBOR, and cash products are dependent on forward-looking term rates. Despite the magnitude of change, transition from LIBOR term rates to an alternative term rate based on limited underlying transactions would be undesirable.
The FSB explained:
Moving the bulk of current exposures referencing term IBOR benchmarks that are not sufficiently anchored in transactions to alternative term rates that also suffer from thin underlying markets would not be effective in reducing risks and vulnerabilities in the financial system. Therefore, the FSB does not expect such RFR-derived term rates to be as robust as the RFRs themselves, and they should be used only where necessary.
In a consultation report published December 20, 2018, ISDA stated the overwhelming majority of respondents’ preference for fallback language with a compounded setting in arrears rate for the adjusted RFR, with a significant and diverse majority preferring the historical mean/median approach for the spread adjustment.
Though ISDA’s consultation report noted some drawbacks to the historical mean/median approach for the spread adjustment, the diversity of supporters – in all regions of the world, representing many types of financial institutions – was a strong indicator of market preference. By comparison, there was no ambiguity about preference for the RFR in fallback language: In almost 90 percent of ISDA respondent rankings, the compounded setting in arrears rate was selected as the top preference for the adjusted RFR.
In the Structured Finance Industry Group (SFIG) LIBOR Task Force Green Paper, the group indicates strong preference for viable term rates and leaves the question of whether such calculations should be done in advance or in arrears as an open item, while indicating preference for continuing prospectively determining rates at the start of each term. They list their preference for waterfall options as first an endorsed forward-looking term SOFR rate, and second, a compounded or average daily SOFR. SFIG is currently drafting their response to the ARRC Securitization Consultation, which will be made public on the ARRC website after submission.
Despite stated preferences, working groups are making a concerted effort to follow the ARRC’s guidance to strive for consistency across cash and derivative products. Given the concerns about a viable term rate, some market participants in cash products are also exploring the realities of implementing ISDA’s recommended fallback language and intend to incorporate those considerations into their response to the ARRC consultations.
In the absence of an endorsed term rate, pricing of other securities such as fixed-rate bonds is difficult, if not impossible. Additionally, the absence of an endorsed term rate creates issues of consistency within the rate itself (i.e., market standards will need to developed around how and over what periods the rate is compounded). The currently predominant recommendation of a compounding in arrears overnight risk-free rate would also have added complexity when compared with any forward-looking rate, which is exacerbated in the cash markets with consumer products where changes must be fully disclosed and explained. Compounding in arrears would require a lock-out period at the end of a term to allow institutions time to calculate the compounded interest. Market standards and consumer agreement around the specific terms governing the lock-out period would be difficult to establish.
Consistency:
While ISDA has not yet completed formal consultation specific to USD LIBOR and SOFR, and their analysis is only applicable to derivatives and swaps, there are several benefits to consistency across cash and derivatives markets. Consistency of contract terms across all asset classes during the transition away from USD LIBOR lowers operational, accounting, legal, and basis risk, according to the ARRC, and makes the change easier to communicate and negotiate with stakeholders.
Though it is an easy case to make that consistency is advantageous, achieving it is not. For example, the Mortgage Bankers Association points out that the ISDA-selected compounding in arrears approach to interest accrual periods “would be a very material change from current practice as period interest expenses would not be determined until the end of the relevant period.” The nature of the historical mean/median spread adjustment does not come without drawbacks. ISDA’s consultation acknowledges that the approach is “likely to lead to value transfers and potential market disruption by not capturing contemporaneous market conditions at the trigger event, as well as creating potential issues with hedging.” Additionally, respondents acknowledge that relevant data may not yet be available for long lookback periods with the newly created overnight risk-free rates.
The effort to achieve some level of consistency across the transition away from LIBOR poses several challenges related to timing. Because LIBOR will only be unsupported (rather than definitively discontinued) by the Financial Conduct Authority (FCA) at the end of 2021, some in the market retain a small hope that production of LIBOR rates could continue. The continuation of LIBOR is possible, but betting a portfolio of contracts on its continuation is an unnecessarily high-risk decision. That said, transition plans remain ambiguous about timing, and implementation of any contract changes is ultimately at the sole discretion of the contract holder. Earlier ARRC consultations acknowledged two possible implementation arrangements:
- An “amendment approach,” which would provide a streamlined amendment mechanism for negotiating a replacement benchmark in the future and could serve as an initial step towards adopting a hardwired approach.
- A “hardwired approach,” which would provide market participants with more clarity as to a how a potential replacement rate will be identified and implemented.
However, the currently open-for-comment securitizations consultation has dropped the “amendment” and “hardwired” terminology and now describes what amounts to the hardwired approach as defined above – a waterfall of options that is implemented upon occurrence of a predefined set of “trigger” events. Given that the securitizations consultation is still open for comment, it remains possible that market respondents will bring the amendment approach back into discussions.
Importantly, in the U.S. there are currently no legally binding obligations for organizations to plan for the cessation of LIBOR, nor policy governing how that plan be made. In contrast, the European Union has begun to require that institutions submit written plans to governing bodies.
Timing
Because the terms of implementation remain open for discussion and organizational preference, there is some ambiguity about when organizations will begin transitioning contracts away from LIBOR to the preferred risk-free rates. In the structured finance market, this compounds the challenge of consistency with timing. For commercial real estate securities, for example, there is possibility of mismatch in the process and timing of transition for rates in the index and for the underlying assets and resulting certificates or bonds. This potential challenge has not yet been addressed by the ARRC or other advisory bodies.
Mortgage Market
The mortgage market is still awaiting formal guidance. While the contributions by Fannie Mae and the FHLBanks to the SOFR market signal government sponsored entity (GSE) support for the newly selected reference rate, none of the GSEs has issued any commentary about recommended fallback language specific to mortgages or guidance on how to navigate the fact that SOFR does not yet have a viable term rate. An additional concern for consumer loan products, including mortgages, is the need to explain the contract changes to consumers. As a result, the ARRC Securitization consultation hypothesizes that consumer products are “likely to be simpler and involve less optionality and complexity, and any proposals would only be made after wide consultation with consumer advocacy groups, market participants, and the official sector.”
For now, the Mortgage Bankers Association has recommended institutions develop a preliminary transition plan, beginning with a detailed assessment of exposures to LIBOR.
How can RiskSpan Help?
At any phase in the transition away from LIBOR, RiskSpan can provide institutions with analysts experienced in contract review, experts in model risk management and sophisticated technical tools—including machine learning capabilities—to streamline the process to identify and remediate LIBOR exposure. Our diverse team of professionals is available to deliver resources to financial institutions that will mitigate risks and streamline this forthcoming transition.
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.
2 “LIBOR – current LIBOR interest rates.” Global Rates. https://www.global-rates.com/interest-rates/libor/libor.aspx, Accessed November 2018.
3 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.
5 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.
7 Alternative Reference Rates Committee. Federal Reserve Bank of New York. https://www.newyorkfed.org/arrc/index.html, Accessed November 2018.
