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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

HOW FINANCE TEAMS CAN UTILISE MODERN TECHNOLOGIES TO PREDICT AND MITIGATE RISK

Carol Lee, CFO of Wrike

 

There is no denying that the finance function plays an important role in every aspect of ‘doing business’. Although much of ensuring strong financial health, tracking revenue, and managing budgets will take place behind the scenes, all are key ingredients which, ultimately, determine whether a business is successful. This is even more relevant in today’s climate.

Thanks to the ongoing pandemic and resulting economic flux, each and every business has faced financial challenges in recent months. As revenues continue to falter, budgets are tighter than ever and profitability is essential.

Amid the economic uncertainty, CFOs and finance teams are set to play an important role in recovery efforts moving forward. Ensuring financial wealth and a solid revenue stream has never been more important. For many, it has also never been more difficult to achieve.

 

Real-time finance

The modern finance team needs to be about far more than month-end and retrospective quarterly reporting. The pandemic has highlighted how important this statement is, with sudden shifts in consumer demand for certain products and services driving drastic changes in revenue for many businesses. For example, at the beginning of the pandemic, many supermarkets will have seen their revenues increase, whilst restaurants and gyms witnessed significant dips following necessary closures.

In order to survive this time of turmoil, finance teams need to be able to quickly and efficiently adapt to these changes in customer behaviour. Planning projects that are expected to yield profit is no longer enough. Finance teams need to ensure that these projects maintain profitability throughout their lifecycle, controlling financials from the planning phase through client delivery. As such, tracking budget spend in real-time in order to keep margins positive and meet customer expectations is key.

Visibility needs to be front of mind, especially in our new remote working landscape, where face-to-face communications has had to take a backseat. The right performance metrics, delivered on time, can enable finance teams to track and obtain a deeper understanding of how projects and finance strategies are progressing and delivering against set objectives. They can help to determine stress points in the business and articulate events and triggers for certain financial actions to be taken.

When utilised alongside the right modern technologies, they can even help to save projects that aren’t delivering, flagging potential problems and recommending where adjustments should be made.

 

Predicting and mitigating risk

Whether it’s unforeseen additional costs, tight margins, or budget burn, these are the factors that can make or break the success of a project and, ultimately, a business. By using real-time insights, finance teams can play a pivotal role in keeping the entire organisation on track. In order to take this one step further and mitigate any potential risks before they wreak havoc, finance teams need to be able to predict and plan for a series of different outcomes. This is where modern technologies, such as artificial intelligence (AI) and machine learning (ML) can help.

Tools with these technologies can help finance teams to get one step ahead and tackle at-risk projects before they cause any issues. By identifying signals and patterns based on hundreds of factors – including past campaign results, work progress, organisation history and work complexity – they provide extremely timely diagnosis and help to minimise risk throughout the entire organisation. For each project, an automated risk assessment prediction will be issued. For both medium and high risk levels, the machine learning model will also provide a list of factors that could contribute to potential delays. The insights that these reports provide can help to save entire projects.

Once a finance team knows what the potential risk might be, they can turn their attention towards what is truly important – managing and mitigating it. This can be done by assessing a project’s ‘risk tolerance’. Put simply, how much risk can you allow before you need to act. This is an essential part of any project management process, helping finance professionals to decide on the most effective response and ensuring that resources are being used in the most effective way.

As organisations across every sector fight to get back on their feet post-pandemic, ensuring long-term profitability will be a key focus. Many businesses will turn to their finance teams to lead the charge and provide the solutions and recommendations which will ensure future economic survival. As such, having a plan in place to make sure that all projects stay on track and that any potential risks to the business are mitigated before they cause a problem needs to be a priority. By investing in modern technologies – such as AI and ML – today, finance teams are setting themselves up for success tomorrow, no matter what is around the corner.

 

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Business

TAPPING INTO THE RIGHT MINDS

David Holden-White, co-founder and managing director, techspert.io

 

The world is awash with information. Analyst house IDC estimated that more than 59 zettabytes of data would be created, captured, copied and consumed in 2020, and that the amount of data created over the next three years will be more than what was created in the past 30. The boom in consumer technology and the rapid improvement in mobile connectivity has meant that the 48% of the globe that owns a smartphone has near instant access to all the digitised, publicly available information in the world in their pocket.

 

A world overloaded by information

It’s no surprise that people talk of information overload, or how much it impacts productivity. It’s not new either. A 2012 study from McKinsey & Co highlighted that nearly a fifth of professionals’ time was spent searching for and gathering information, half of the time they spent undertaking role-specific tasks. This is only likely to have increased as we’ve become more dependent on digital tools and services.

On top of that is the realisation that, thanks to social media, we’re living in a time when anyone can be an influencer or thought leader if they shout loud enough. It doesn’t matter whether you’re pushing trainers or cloud computing, whether your audience is a broad spectrum of consumers or a niche group of B2B buyers; the tools and resources are pretty much freely available to build a profile and push your message out there.

David Holden-White

The result is that it’s becoming increasingly hard to find the value amongst vast and accelerating volumes of online data and noise, and to use that data to make accurate, effective decisions.

This is something we need to be able to do. We’re all expected to work faster, to make better decisions more quickly. The pandemic showed that certain changes don’t need five committees, two working groups and a proof of concept to take place before decisions can be rubber stamped. At the same time, no matter what industry you work in, there will be competitors who are more agile, more flexible, and seem to be much better at making decisions and capitalising on opportunities.

Yet those decisions still need to be backed by evidence, by irrefutable knowledge. What’s more, there’s only so much data can give us. We need the insights stored in the minds of true experts, with lived experiences of the particular problems, markets and technologies in question. In accessing this, we can develop a decision-making edge in businesses that competitors don’t have, that can be used to drive entrance into new markets, or for winning investment decisions.

 

Limiting risk in investment decisions

As we all know, investments are inherently risk-related, so, anyone making such a decision will do all they can to minimise their risk exposure, especially in volatile post-covid markets.

To do that requires being able to identify, consume and process information quickly. Investment opportunities, particularly in industries with significant growth capacity, come around quickly and get snapped up fast.

Those decisions will incorporate analysing and drawing insights from raw data, using publicly available and analyst-produced information. But there is also an opportunity to draw on human insights, from leading experts in relevant fields, to get a sense of the story that 0s and 1s can’t properly tell yet. Tapping into the right minds  is essential to informing investment decision-making in 2021.

In an ever-growing haystack of information, the challenge is finding them quickly. Plus, once they are found, there’s a tendency to keep using them, or to use them as a gateway to others in their network. While there’s nothing inherently wrong with this approach, it leaves investors exposed to a lack of diversity in thought that makes getting to an unbiased view of the world impossible. At the same time, casting their net wide and finding lots of experts is resource and time-intensive, at a point when time is one commodity in short supply.

So, what’s the solution? Ironically, given that the challenge is bringing the right human insight into the process, the answer could lie in technology, specifically artificial intelligence (AI). AI-powered platforms can take a request for expertise and run searches through all available published and credible material to recommend the most appropriate experts for the project in question.

It’s true that there are already services that recommend experts, but they are heavily manual and therefore slow and imprecise. It’s also true, there are also both negative and positive connotations being attached to AI. No technology is without its flaws, and if investors were relying on the AI platform itself to provide expertise then there would be cause for concern. Services that provide access to the experts themselves, however, are providing a fast way through the noise and data – it’s a car to the destination, not the destination itself. Once investors and experts are connected, the former has access to the relevant insight the latter holds in their heads. What AI has done is rapidly scan through millions of people of talent to highlight the relevant knowledge holders with pin-point accuracy.

 

Using technology to highlight the best human knowledge

Using an AI technology platform to find the most relevant human is a way of taking a resource-consuming process and finding what’s needed in a thousandth of the time. In that way, investors can get fast access to the human insight they need to make the best decisions,  allowing them to capitalise on opportunities and not miss the next big growth opportunity.

 

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