New transaction structures and increasing complexity have continued to proliferate in the loan markets for some time. As a result, data and reporting needs have also become more involved, especially over the past three years. Increased investor demand for data and reports, as well as higher scrutiny from regulators are also playing a part in changing the needs of loan market participants.
Many of these changes stem from loan data user needs (such as investment funds, CLO managers, underwriters, middle-office providers, custodians, and trustees). However, they also impact the providers of such data—namely, loan agents who provide core loan processing data and loan market data providers of broader credit data—due partly to the increasing activity of private lenders in the commercial loan market. Therefore, it is essential to look at the question of data and reporting from the perspective of the loan markets overall as well as specific users.
On the technology side, delivery channels including client portals, secure file transfer protocols (SFTP), and application programming interfaces (APIs), alongside data management tools such as big data warehouses and Artifical Intelligence (AI), offer increasing potential to all participants involved in the loan markets. But our point of view is that data and reporting are, first and foremost, a question of workflow, which technologies can then support or enable.
Together, all of the forces that have brought data and reporting to the top of the agenda in the loan markets raise the bar for the collection of data, the trustworthiness of data, and the ways providers package data for delivery. In other words, sophisticated data technologies are not a cure-all. Focusing only on technology can sometimes become a distraction, with features that fail to match what end users need to accomplish.
More Complexity, New Demands
Factors such as the progressive rise of private credit and the maturity of the CLO market have played a significant role in raising the bar for loan reporting and data. Private debt assets under management (AUM) worldwide have more than quadrupled to $1.3 trillion between 2010 and 2022.1 Banks’ percentage share of participation in the leveraged loan market has stayed in the mid-teens since the Global Financial Crisis, per S&P data.2 The global CLO market is at roughly $1.3 trillion as of June 2023.3
In other words, there are more investors with greater sophistication and aggressive demands for understanding how their loan assets are performing. As structures become more complex and layered, investors leverage data to inform their investment decisions and overall strategies. That trend is pushing the loan market to move towards real-time information and reconciliation.
CLOs are a case in point, but it is important to call out that more robust loan data expectations do not end there. The evolution in structures and documents from early vintage 1.0 CLOs to today’s 3.0 CLOs imposes more risk testing requirements. The requirements depend on specific data tracking from industries, spreads, and countries to a much broader set of information such as credit stats, EBITDA, and cash flow for investors to digest—which are typically delivered via trustee/custodian reporting supported by underlying manager data.
In the EU, the European Securities and Markets Authority (ESMA) has introduced additional requirements for CLOs to increase their level of transparency with specified data fields and standard reporting templates related to transaction documentation and more robust risk characteristics. The details of these requirements are a separate topic, but the broad implications have a knock-on effect for loans data, whether for EU investors in CLOs or for the market as a whole.
ESG has also become more topical as investors are focusing more on allocating capital to investment strategies that support socially responsible companies/ industries, especially in Europe.
Finally, in the wake of LIBOR cessation, loan market stakeholders will have to contend with a new multi-rate environment. As a result, we will see additional layers of complexity in data reporting, tracking, processing, and investor analysis as loan vehicles transition to post-LIBOR rate conventions.
Combining loan data from multiple loan agents in addition to various credit and post- closing data sources is challenging. A typical scenario: an asset manager often wants automated, real-time reporting across all of their positions, all serviced by different agents. We are long past simpler times of straight term loans and revolving facilities; complex structures and multiple tranches are today’s norms.
Investors want comprehensive compliance tests reporting across all of this data, which in turn requires a high level of effort in reconciling data across the many individual loan positions within a funding structure. The rate of change in investors testingand reporting needs differs across structures such as CLOs, Asset-Based Lending, Separately Managed Accounts, Private Funds, and Business Development Companies.
While such expectations would sound relatively straightforward in other asset classes such as bonds, derivatives, and equities, significant data gaps exist in loans. They have not yet been resolved in a uniform way across the entire loan market.
As a result, CLO managers and asset managers participating in loans must reassess their operational infrastructure for broadly syndicated loans, middle market, distressed debt, and other lending types. Collaboration with and among outsourcers, data providers, trustees, and agents are essential to addressing these gaps.
Mind the Gaps
Lack of data standardization in loan markets is a fundamental reality. There is no market-wide data governance or general repository. A global industry-wide set of standards would be many years away and may even be unrealistic to expect. Practical solutions that circumvent standardization gaps make much more sense, but we are also very far away from having real-time, fully automated reconciliation.
For example, common identifiers for loans—data and information about those loans do not reliably tie back to the equivalent of a security master such as a CUSIP or ISIN. Market data providers can have overlapping but incomplete coverage of loans, with no guarantee of consistent identifiers or equivalency in data. While some progress towards standardization has occurred with the European Central Bank’s AnaCredit initiative, data coverage is not global. These gaps widen in the middle market segment. Data formats for loan agent notices are another such example.
There are also gaps in data timeliness. Some providers post data on a pre-closing basis and do not include last-minute shifts that could impact trading decisions. Some donot fully capture rating or leverage grids, which can introduce additional issues around accurate spreads. Post-LIBOR rates and accrual calculations will only add to such confusion. Moreover, information such as cash and position data is typically received on a T+1 basis, and the timing of information about loan amendments can be quite spotty.
Finally, we see wide dispersion in the means delivery of loan information from both agents and other providers to managers. Solutions such as web-based portals, SFTP, and APIs have still not dislodged emailed PDFs or even faxes and phone calls.
The cumulative effects of these gaps constrain asset managers’ ability to use accurate, real-time information to make decisions about loan-based assets in their portfolios.Data gaps make it harder to understand and optimize performance, and create frustration among end investors, too.
While we have seen excellent results with rules-based machine learning to date, we are constantly iterating and improving upon it. The use of AI has not matured enough to deliver self-learning reconciliation that adjusts to unexpected new variations. Machine learning and optical character recognition have managed to produce some improvements, too, but wide gaps between the delivery of this information and its ingestion by managers remain. We discuss these further below.
Maybe You Can, But When Should You?
Digitized information, real-time delivery, portals, APIs, smart contracts on the blockchain, AI—there is a solid list of high-power, high-potential, high-promise technologies available for loan market stakeholders to consider adopting. Wilmington Trust is actively engaged in leveraging these technologies to improve efficiencies, manage risk, and better serve our client base.
Making sure that digital solutions map well to real-world workflows requires special consideration. For example, a web-based portal can provide a helpful window into loan and portfolio data, but it does not always provide additional efficiency. In many instances, portals present stale data, thereby introducing further reconciliation items that may have already been resolved but have not yet appeared in the portal.
Many of these factors contribute to the low adoption of technologies such as portals within the industry. When applied to the proper use cases, such as viewing a report or allowing a borrower to view all lenders participating in a facility, portals can deliver meaningful self-service value. In a sense, clients are trying to keep the portal up-to- speed, not the other way around.
An API can help eliminate rework and reduce human error by directly ingesting loan data, but data delivery time may still not match an asset manager’s trading frequency. APIs typically push data to client systems a limited number of times per day, sometimes as infrequently as once daily.
Since an asset manager’s entire loan portfolio requires information from multiple providers, dealing with various portals and APIs may ultimately just replicatethe gaps of more manual data delivery in digital form. Operations staff still have significant work to do to ingest and reconcile. Loss of operational control means loss of information control, leaving asset managers dependent on an SLA that may not deliver what they need.
Meeting the Needs of End Users: Closing the Gap
Successful implementation, integration, and adoption of technologies can only help close the information gaps in the loan markets when tailored to the way people want to use the data. Technologies in search of a solution have an even harder time looking for real-world usage. Instead, data providers must work to adapt both the data they provide and the way they provide it. In our experience, that flexibility is essential to meeting the specific needs of clients and their end investors.
Self-service can streamline searching, lookups, and ad hoc report creation in theory, but in practice, loan agents still receive thousands of emails per year for simple requests such as position confirmations. The old ways of doing things are hard to dislodge, especially without mass adoption of standardized encoded data formats across the entire industry.
Looking at human factors also changes the role of a data provider, which is simply another way to describe the roles of a loan agent or a CLO trustee/custodian. Data providers can work collaboratively with users of that data to develop solutions tailored for their specific needs across a wide range of areas, from cash and position reconciliations to exception handling to critical month- and quarter-end processing. Communication is ripe for deeper electronification to support better, mutually beneficial interaction between providers and stakeholders.
The goal is offering more forms of reconciliation at increased frequency while developing efficient channels to transmit and ingest data into key systems. That approach improves overall workflow. As a result, it helps asset managers be better prepared to meet their needs.
Conclusion
Neither behaviors nor technologies will change overnight. Familiar ways of doing things have a lot of staying power. While we see the loan markets on a long-term trajectory to standardize around real-time data and shared industry-wide conventions for reporting, it will take many years to get there.
As that progress unfolds, loan agents, CLO trustees, and other data providers will serve clients best by offering technically robust solutions, designed to be both workflow- and people-aware.
Equal Housing Lender. Bank NMLS #381076. Member FDIC.