Business

WHY LIBOR RETIREMENT IS THE NEW Y2K

Published

3 years ago

August 22, 2020

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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

Unlocking the Power of Data: Revolutionising Business Success in the Financial Services Sector

Published

18 hours ago

June 8, 2023

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Suki Dhuphar, Head of EMEA, Tamr

The financial services (FS) sector operates within an immensely data-abundant landscape. But it’s well-known that many organisations in the sector struggle to make data-driven decisions because they lack access to the right data to make decisions at the right time.

As the sector strives for a data-driven approach, companies focus on democratising data, granting non-technical users the ability to work with and leverage data for informed decision-making. However, dirty data, riddled with errors and inconsistencies, can lead to flawed analytics and decision-making. Siloed data across departments like Marketing, Sales, Operations, or R&D exacerbates this issue. Breaking down these barriers is essential for effective data democratisation and achieving accurate insights for decision-making.

An antidote to dirty, disconnected data

Overcoming the challenges presented by dirty, disconnected data is not a new problem. But, there are new solutions – such as shifting strategies to focus on data products – which are proven to deliver great results. But, what is a data product?

Data products are high-quality, accessible datasets that organisations use to solve business challenges. Data products are comprehensive, clean, and continuously updated. They make data tangible to serve specific purposes defined by consumers and provide value because they are easy to find and use. For example, an investment firm can benefit from data products to gain insights into market trends and attract more capital. These offer a scalable solution for connecting alternative data sources, providing accurate and continuously updated views of portfolio companies. Using machine learning (ML) based technology enables the data product to adapt to new data sources, giving a firm’s partners confidence in their investment decisions.

Suki Dhuphar

But, before companies can reap the benefits of data products, the development of a robust data product strategy is a must.

Where to begin?

Prior to embarking on a data product strategy, it is imperative to establish clear-cut objectives that align with your organisation’s overarching business goals. Taking an incremental approach enables you to make a real impact against a specific objective – such as streamlining operations to enhance cost efficiency or reshaping business portfolios to drive growth – by starting with a more manageable goal and then building upon it as the use case is proved. For companies that find themselves uncertain about where to begin their move to data products, tackling your customer data is a good place to start for some quick wins to increase the success of the customer experience programmes.

Getting a good grasp on data

Once an objective is in place, it’s time for an organisation to assess its capabilities for executing the data product strategy. To do this, you need to dig into the nitty-gritty details like where the data is, how accurate and complete it is, how often it gets updated, and how well it’s integrated across different departments. This will give a solid grasp of the actual quality of the data and help allocate resources more efficiently. At this stage, you should also think about which stakeholders from across the business from leadership to IT will need to be involved in the process and how.

Once that’s covered, you can start putting together a skilled team and assigning responsibilities to kick-off the creation and management of a comprehensive data platform that spans all relevant departments. This process also helps spot any gaps early on, so you can focus on targeted initiatives.

Identifying the problem you will solve

Now let’s move on to the next step in our data product strategy. Here we need to identify a specific problem or challenge that is commonly faced in your organisation. It’s likely that leaders in different departments, like R&D or procurement, encounter obstacles that hinder their objectives that could be overcome with better insight and information. By defining a clear use case, you will build a real solution to a challenge they are facing rather than a data product for the sake of having data. This will be an impactful case study for your entire organisation to understand the potential benefits of data products and increase appetite for future projects.

Getting buy-in from the business

Once you have identified the problem you want to solve, you need to secure the funding, support, and resources to move the project ahead. To do that, you must present a practical roadmap that shows how you will quickly deliver value. You should also showcase how to improve it over time once the initial use case is proven.

The plan should map how you will measure success effectively with specific indicators (such as KPIs) that are closely tied to business goals. These indicators will give you a benchmark of what success looks like so you can clearly show when you’ve delivered it.

Getting the most out of your data product

Once you’ve got the green light – and the funds – it’s time to put your plan into action by creating a basic version of your data product, also known as a minimum viable data product (MVDP). By starting small and gradually enhancing with each new release you are putting yourself in the best stead to encourage adoption and also (coming back to our iterative approach) help you secure more resources and funding down the line.

To make the most of your data product, it’s essential to tap into the knowledge and experience of business partners as they know how to make the most of the data product and integrate it into existing workflows. Additionally, collecting feedback and using it to improve future releases will bring even more value to end users in the business and, in turn, your customers.

Unlocking the power of data (products)

It’s crucial for companies in FS to make the most of the huge amount of data they have at their disposal. It simply doesn’t make sense to leave this data tapped and not use it to solve real challenges for end users in the business and, in turn, improve the customer experience! By adopting effective strategies for data products, FS organisations can start to maximise the incredible value of their data.

Business

Making the Maths Work: Addressing Inflation Challenges through Measuring and Managing Risk

Published

1 day ago

June 8, 2023

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Matt Clementson, Head of Enterprise UK&I

Persistent inflation is highly troublesome for every business – with or without a recession. In addition to causing unexpected expenses, it complicates decision-making around stabilising wages, setting product prices, and investing in new areas for growth. Meanwhile, stock and bond prices plummet when alarming inflation data arrives and interest rates increase. It’s time to run leaner, making the reassessment of the strategic objectives highly urgent.

With a seat in the boardroom, CFOs can guide thoughtful discussions covering everything from procurement, resource allocation, and manufacturing to the alignment of business purpose with operational tactics and goals. CFOs must also rethink how their business measure and mitigate risk. Understanding the business’ vulnerability, they can add considerable value to their business by identifying risks early and making organisations accountable for mitigating them.

When the economy becomes uncomfortable, the mathematics behind business operations no longer work seamlessly. During more comfortable times businesses have the luxury to accept some degree of inefficiency and low productivity – but in times like these that’s no longer the case.

So now it’s more important that ever for CFOs to use the right tools and technology to manage and mitigate risk and build business resilience.

Enhancing visibility to measure and manage risk:

To navigate through periods of high inflation, CFOs need technologies that provide comprehensive visibility, and enable informed decision-making, in order to optimising cash flow, minimise costs and manage risk in a transparent and efficient way.

1. Simplify confusing processes to gain moments of clarity

Effective risk management starts with integrating data from various sources within the organisation. By consolidating data from finance, operations, procurement, and sales, CFOs can gain a holistic view of the business landscape. This integration enables them to identify potential risks associated with inflation, such as rising costs, supply chain disruptions, or changes in customer demand patterns. With access to comprehensive and real-time data, CFOs can make informed decisions that mitigate the impact of inflation on the organisation.

A good first step is to unify travel, expense, and invoice solutions, so that finance teams can integrate and streamline operations and scale spend processes without adding additional resources.

2. Make spending decisions with data-driven accuracy

Once data is integrated, CFOs can leverage advanced analytics techniques to identify patterns, trends, and potential risks. Predictive analytics can help identify inflationary pressures, allowing businesses to proactively adjust pricing strategies or negotiate favourable terms with suppliers. Additionally, scenario modelling can simulate the impact of different inflation rates on the organisation’s financials, enabling CFOs to devise appropriate strategies for managing risk. By harnessing the power of analytics, CFOs can navigate inflation challenges with greater confidence and precision.

3.Driving business agility through automation

Facing a myriad of disruptors, companies in every industry are making strategic decisions aimed at remaining competitive in the market and with their people. Digitisation, standardisation, and automation will be critical as businesses focus on solving problems for their customers in innovative, lasting ways

AI technologies, such as machine learning algorithms, can analyse vast amounts of data to uncover hidden insights and patterns. And with automated, customisable controls, CFOs can keep their firm agile – re-adjusting spend controls to match the corporate travel and expense (T&E) policy whenever their business needs to adapt or pivot. Only then will spending insights allow them to review how policies impact business performance and continue to optimise cash management.

Making the maths work

In a business environment plagued by persistent inflation, CFOs play a crucial role in addressing the associated challenges. By rethinking how their organisations measure and manage risk, CFOs can enhance their decision-making capabilities and add significant value. The integration of data, advanced analytics, and AI technologies enables CFOs to build resilience, standardise processes, ensure compliance, and deliver insights to the entire enterprise. By making the maths work in the face of inflation, businesses can navigate uncertain economic times with confidence and stay on the path of sustainable growth.