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WHY LIBOR RETIREMENT IS THE NEW Y2K

SOFTWARE

Byline: Dihan Rosenburg, Director, Product Marketing at ASG Technologies

 

With the coming December 2021 retirement of LIBOR, Chief Data Officers in financial institutions are preparing to face their Y2K moment. An estimated $350 trillion of financial instruments are linked to this globally accepted benchmark rate for short-term loans, including adjustable rate mortgages, credit cards, student loans, bonds, securities and more.

The data management challenge is colossal. Over 100 million transactions reference LIBOR and must be replaced with alternative risk-free rates (“RFRs”), such as SOFR for the U.S. dollar and SONIA in the UK.  Making the switch won’t be a simple search-and-replace exercise. Here’s why:

  • RFRs are overnight rates, whereas LIBOR is published for multiple terms (e.g., one-week, three-month, etc.).
  • Credit risks are embedded in LIBOR, while RFRs are “risk-free,” making a simple conversion impossible.
  • RFRs have different behavioral characteristics than LIBOR, resulting in different historical spreads, so fallback rates must be applied.

To make the transformation, contracts referencing LIBOR will all need to be rewritten. Organizations must locate all references to LIBOR in contracts they hold to update with fallback provisions reflecting the new RFR terms, and communicate those new terms to clients.

While some banks have made a start on contract remediation, the data impact is even broader. Firms will need, for example, to create new models for pricing using the new benchmarks, creating new credit risk spreads and evaluating how that will affect margins and profitability—and far fewer firms have yet reached that point. Firms will also need to consider the impact of these new benchmarks on their FRTB and BCBS 239 programs.

 

To Get it Right, Impact Analysis is Vital

The transition of this complex data journey requires robust and comprehensive inventory analysis and data lineage capabilities. This upfront impact analysis is necessary to find the rates and analyze the changes before replacing them.

While firms may try to manage the necessary analysis manually with spreadsheets and subject matter expert (SME) interviews, this approach is particularly impractical for LIBOR migration programs. LIBOR has been around for over 40 years. The rates are primarily sourced and calculated inside of legacy systems—many of these SME’s have long since retired.

Even locating all the references to LIBOR rates will be difficult. The data is spread across unrelated systems and technologies, data will be both unstructured and structured, contracts may be unlinked to amendments, or a host of other data disparities may present themselves. Tracing LIBOR data is also complex, due to inconsistencies in how data is organized at the logical and physical level and transformed across thousands of applications.

 

Fortunately, Metadata Management Applications Can Automate these Tasks

Automated metadata harvesting and management applications are ideal for the unique data management challenges LIBOR migration presents. Here’s how:

  • Automated inventory will accelerate the discovery of where LIBOR rates are referenced, whether in applications, mainframes, ERP systems or contracts.
  • Automated data lineage graphically displays the flow of LIBOR data from its origination in legacy platforms to all final points of consumption—identifying transformation points and systems where derived values are calculated along the way. Intelligent lineage delivers both vertical and horizontal views, offering a tangible connection between the business and technical metadata. It provides a visual, easily digestible artifact that informs the analyses of technology, business and risk leaders.
  • Business glossaries document business processes and terminology and connect technical and business assets. An artificial intelligence (AI)-based recommendation engine (“virtual data steward”) accelerates the speed and accuracy of matching business and technical data, as well as helping to find and track new rates going forward.
  • Impact analysis begins with an understanding of how LIBOR rates are being calculated today. Analysts will find these calculations embedded inside of the SQL code from within the data lineage. The Snapshot feature displays both the current rate and what it will look like post-conversion to test and validate the new piping. By monitoring the lineage as it begins to shrink, users will also be able to track their progress towards meeting the deadline.
  • Data quality information can augment the data lineage analyses to ensure key areas get the attention they require. Other data governance features—including data stewardship, issue management and dashboards—will keep disparate teams informed and coordinated across various concurrent activity streams.

 

From LIBOR Transition to Digital Transformation

Typically, financial institutions have treated similar data initiatives like MiFID II, Dodd Frank and Margin Rules as one-off projects. Disruptions in the global business and regulatory environment are occurring with increasing frequency mandating a more holistic approach. For instance, LIBOR is only one of the reference rates being retired. Other interbank rates (“IBORs”) around the globe are also on the chopping block.

Instead of viewing the LIBOR transition as just another costly, resource-consuming exercise, organizations should see this change a catalyst to uplevel and automate their data management processes to better understand and trust their data.  Here are some benefits that one large financial institution told ASG they received from implementing an automated data intelligence system in just four months:

  • Discovered LIBOR rates across 150 Applications and 20,000 Cobol programs.
  • Solved a nine-month attestation request from their largest client that was being passed from department to department in search of a root cause.
  • Shrunk their footprint of redundant applications, including consolidating ten UDTs to one!

With these successes, this data governance organization was able to easily justify additional investment for other use cases.

By looking at this transition as an opportunity to transform enterprise data intelligence and put in place the processes needed to clean, understand and trust data, financial organizations can future proof their data and adeptly address emerging changes. And with the greater data usage that trusted data enables, they can adroitly exploit new opportunities to enhance operations, deliver greater value to customers and gain competitive advantages.

 

Business

BACK TO SCHOOL – CEOS NEED TO LEARN A NEW LANGUAGE, FAST!

By Simon Axon, Financial Services Industry Consulting practice lead in EMEA, Teradata

 

Chief Executive Officers of banks know all about change. Leading responses to new challenges, new opportunities, new regulation and new markets is all in a day’s work. But the existential challenge posed by Big Tech requires a totally new set of skills. It is an entirely different beast that inhabits a totally new environment and speaks its own language. CEOs now need to learn the language of data to survive in the emerging digital world.

Learning a new language later in life is hard. CEOs need to fully commit to accomplish it. Becoming data literate means mastering the basics of vocabulary and grammar. Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.” Extending the language analogy: the building blocks are an understanding of logical data models – the basic vocabulary; meta data providing rules and information about data is the grammar.  Learning needs to go beyond parroting a few key phrases and acronyms. To really communicate in this new language CEOs must not only be data literate – but data cognitive. Language shapes thinking, and to succeed, today’s CEOs need to think data like digital natives.

Simon Axon

As anyone who has learned a language will recognise – practise makes perfect. This means rolling up your sleeves and getting into the data ‘lab’. Run some queries, experiment with data to test theories and learn how data can, and should, inform all aspects of business management. It is daunting, and different functions are fiercely protective of their data. But that’s one of the big cultural shifts the CEO needs to lead. Data is more valuable when it is used across the business. Developing safe and secure ways to combine, refine and analyse data at an enterprise level is fundamental to competing with Big Tech. The Chief Data Officer can help. Spend time with them and use them as a teaching-resource to get more familiar with what can and cannot be done with your data.

As you practise you will build confidence and move from school-level conversations to business-class data fluency. Spending more time looking at and working with data and you will begin to recognise ‘quality’ data, identify attributes and flag anomalies. This will build confidence and essential trust in data. Last year KPMG found just 35% of CEOs trusted the data in their organisations. This shocking stat undoubtedly stems from a data skills deficit among CEOs themselves. If they don’t know what to ask for, and can’t recognise what they get, they won’t trust it. To stretch our linguistic analogy, if you are not confident in the language then you’ll be anxious ordering food in a restaurant!

Ultimately, no one expects the CEO to personally implement data-analytics programmes across the business. But unless they have the confidence and the skills to accurately communicate what’s needed, to sit at the head of the table and ask the right questions about the menu, then the organisation is unlikely to put the right emphasis on the data strategy.

In How Google Works, former Google Chairman Eric Schmidt outlines how every meeting revolved around data – it is simply how Big Tech works. Banks need to adopt the same approach. Exploiting data in all scenarios must become second-nature. By modelling the use of data across the business – dissolving silos rather than sticking to narrow data sets that reinforce them, the CEO can define a powerful data culture. Operationalizing data strategy will, just like using language skills, stop data literacy from becoming rusty.

Entering any new market requires investment in understanding the language, culture and business environment. In the Big Tech world, data is the lingua franca informing every decision. Bank CEOs need to learn from them and invest in building their knowledge to become data fluent. There are no short cuts. Throwing money, bodies and tech at the problem will not get you there.

 

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Business

REVITALISING THE TOKEN MARKET

By Gavin Smith, CEO at Panxora

 

With interest rates near zero and fears that whipsawing stock markets are set for further plunges, many investors are turning to alternative markets in the search for returns. Money flowing into cryptocurrency hedge funds and trusts like Grayscale is at all-time highs and the large cap coins seem to be entering a bull phase, but that capital is not trickling down into new token projects. Why are blockchain token projects struggling to attract funding?

 

Seed investor scepticism

Setting aside the reputational issues with mainstream investors, even those educated in blockchain tech are not signing on the dotted line. This is certainly due in part to the hangover from the early token market.

During the heady days of 2016/17, investors could buy tokens during the token sale, and if the project was legitimate – even if the business case wasn’t particularly strong – prices would soar based on market enthusiasm. Early investors purchased at a discount and cashed out almost immediately for a handsome profit – and then repeated the process again. The token sale allowed founders to amass a war chest large enough to finance the entire token project – without having to give up a large chunk of company equity. Everyone got what they needed out of the deal.

Running a token sale is far more expensive today than it was during the boom. Getting the attention of the token buying public in a market where advertorial has replaced editorial is expensive. This coupled with a regulatory framework that requires the advice of accountants, solicitors and information gathering of KYC details for investors all comes with an escalating price tag.

To accommodate the change in cost structure, tokens now need to acquire funding in two rounds. Frequently there is a first round where capital is raised from a few, large investors. This cash is then used to finance setup and marketing the main token sale. The token sale, in turn, provides the capital needed to run the entire business project.

 

Bridging the gap between token projects’ needs and early stage investors

To successfully get a token through the capital raising process, founders must acknowledge the risk assumed by those very early investors and reward them appropriately. And given that tokens may stagnate or fall in price post token sale means that a deep discount in token price is not necessarily attractive enough to get investors to commit.

Many tokens have turned to offering equity in the business in the effort to raise that first tranche of capital. If you look at the number of successfully concluded token sales, the downward trend has continued since Q2 2018, so offering equity is not sufficiently stimulating the market.

 

Two sides of the coin

So, what is the answer? It’s a complex question but one thing is certain. Any solution must be rooted in a deep understanding of what both parties need to successfully conclude the deal.

On the one hand, token founders’ needs are clear: they need enough capital to get the token ready for and through a successful liquidity event that will provide sufficient funds to build the project. The challenge lies in striking the right balance between accruing that capital and making sure not to offer so much project equity that give up either the control or the incentive founders need to drive the project forward.

On the other hand, while the needs of the seed capital investors are more complex, there are two areas of key concern: transparency and profit incentives.

 

Transparency can mean many things, but almost always includes providing more informative cost and profit projections, as well as answers to a whole range of questions, not least the following:

  • What happens to investor capital if the token sale event fails? Token founders must be transparent from the outset. The token market is highly speculative and early investors run the risk of losing their money should the project fail. Therefore, investors require a well-established fund governance process in place throughout the fundraising so they can make informed decisions on whether the project is worthwhile. 
  • How are the assets for the entire project managed? Investors need to know that their money is in good hands and that proper treasury management techniques are being used to manage cryptocurrency volatility risk. Ideally, an independent custodian will be used to hold the funds and limit founders’ ability to draw down the capital – releasing funds to an agreed-upon schedule of milestones.
  • How are the rights of investors protected, for instance in the case of a trade sale? Investors need to know what happens if the company they are investing in is sold. What impact could this have on the value of their stake? Would a separate governance framework need to be established? These are critical questions and investors aren’t likely to settle for any ambiguity in the answers.

Profit incentives are important when it comes to encouraging early participation in a project. Investors need convincing that the proposition will keep risks to a minimum and focus on providing a strong probability of a return. This means that founders need to be able to defend the case for the increase in the value of their token.

But this isn’t the only incentive that matters. Investors can also be incentivised by preferential offerings such as early access to projects and services that might help their own business.

Let’s not forget that investors don’t support just any project. What really matters is that there is something special and unique about the business being underwritten by the token. Preferably something that could be shared upfront and directly benefit the investor – proof that the investment is really worth it.

And that’s what it all comes down to. Ultimately, while token projects are having a hard time finding funds at the moment, if they can prove their worth and provide full transparency and clear profit incentives to ease investors’ concerns, the money is out there. And deals can be done.

 

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