Warehouse and term lending facilities provide capital to originators and investors who look to fund and/or purchase loans, leases and other receivables of varying asset classes, which may or may not be appealing to certain types of bank and non-bank financial institutions. This growing type of financing is an attractive alternative to direct lending helping to facilitate a strong return on investment. Lender financing is critical as it provides credit to several industries that support supply chain; transportation, real estate (particularly investor rental, fix and flip & commercial properties), and employment (small & medium businesses) markets.
While this type of lending is attractive, facility providers increasingly face challenges associated with rising rates, more regulation, borrowers looking for flexibility, and the need to ensure proper risk management and operational controls. These challenges are exacerbated as the size of a program grows. In this paper, we examine what it takes for lender/facility providers to scale appropriately, while being able to balance borrowers’ need for flexibility with their own internal requirements for managing risks.
Lending facility providers face many operational challenges that make it difficult to grow and scale while maintaining proper controls. In many respects, it is simply the nature of the business given the model involves providing financing in unique and flexible ways. Market forces such as inflation and rising interest rates magnify both theses challenges. The most fundamental challenge occurs due to the nature in which lender financing is provided. That is, considering the competitive landscape across the market, lending facility providers will generally choose to either create a borrowing base on behalf of their clients or accept borrowing base reports and settlement statements with limited to no standards. Both situations create a significant challenge as the form and format of the data provided by originators/borrowers varies greatly, lacking standardization. This leaves lending facility providers with the task of having to collect, validate, normalize, and transform the information so that proper surveillance and decision-making occurs. This is even more onerous in the fact that often new, unique eligibility and compliance rules, along with complex advance rate calculations, are at times utilized to ensure deals close. Likewise, the demands around credit monitoring and compliance increase as market conditions become more challenging.
So, what are some of the key considerations for a lending facility provider to take into account in order to position its business for growth while maintaining proper controls and risk management? First, having the right systems in place to automate the ingestion of data from originators/borrowers is foundational. However, this is easier said than done, as data collection for this market is not trivial. In fact, lenders require tools which are capable of handling many different types of file formats, can ingest both loan level and aggregate level data, are able to automatically support the inevitable movement in where certain fields are provided. Also, lenders would be well placed to consider a mechanism to streamline the actual data submission from borrowers. As the size of a portfolio grows, it can become impractical to have analysts manually upload files received from borrowers via email. Not only is this inefficient given the time to complete manual support, but also creates an unnecessary human error risk.
Likewise, consideration of how a lender will nomalize data received from its borrowers is extremely important. Borrowers are inconsistent in how they report items, even for the most straightforward of fields such as what’s determined to be an actual borrowing base, let alone the names they use for given industries or geographic locations. Imagine, therefore, trying to do an exposure analysis by industry across facilities without having some standardization of a given industry name. As such, to effectively manage risk and perform analyses, having a mechanism to normalize data elements of all types is critical.
Once the data is collected, ingested and normalized, a lender must consider the fact that there can be a significant amount of data for analysts to review. Take, for example, a situation where a borrower has a funding request. To truly analyze such a request, several levels of diligence should be done. While the type and breadth of the analysis can differ based on many factors, the reality is that lenders are welll served to consider technologies which move their team(s) out of the business of analyzing the entirety of the request to one that is more exception driven. To highlight a common example, rather than analysts looking at each individual loan and validating the data or even looking at all aspects of the borrowing base, the workflow could be improved by moving to a process whereby technology itself can highlight exceptions. This consideration is important for internal scalability and for “customer” service having the right capabilities, which can also help ensure that lenders meet faster turn-around times.
Moreover, lenders have with a variety of reporting obligations as well as general requests for information. This not only includes individual facility level reporting, but also cross portfolio type updates that must be provided to a variety of stakeholders. Some of these items are repetitive. For example, a management report which shows the status of each of its facilities at month end. On the other hand, some items are more ad-hoc in nature. A good example may be a request by management related to the exposure to a given borrower. Given this, lenders must consider how to not only automate reporting, but also enable quick access to ad-hoc requests. While some of this could be done through manual or partially automated Excel spreadsheets, the fact of the matter is that an approach such as this is not scalable even if all of the aforementioned data ingestion and normalization challenges are addressed.
Finally, there are instances in which lenders act as the primary generator of the borrowing base/investor report and others where the lender wishes to fully shadow the information reported by the borrowers. This is not an easy task and even once the data is normalized requires the capability to model various eligibility criteria, compliance rules and advance rates. While there are sometimes similarities across facilities, the fact is that in order to “win” deals, lenders must provide different terms and conditions to different borrowers. This of course is good for business growth, but represents an operational challenge for those responsible to attest to the accuracy of these results. Therefore, very important consideration must be given to having technology to support this expanding practice. Similar to reporting, an initial “answer” may be to use Excel, however this approach tends to lead to many challenges, inefficiencies, lack of proper controls, and is extremely difficult to scale.
While there have been several challenges mentioned above, certain technology solutions do exist to help lending facility providers. Given the nuances and ever evolving requirements of the market, choosing the most advantageous technology and partner to deliver it, is absolutely critical.
Moody’s Analytics is the correct partner in this space because of our technologically advanced platforms. With decades of experience across a broad spectrum of markets including lending and structured finance, Moody’s Analytics and its Lender application is uniquely positioned to provide lending facility providers with the capabilities needed to address such challenges. Through our Lender platform, we provide lenders the ability to scale growing term lending programs across a myriad of asset classes.
Lender developed from Moody’s Analytics’ market leading position of providing operational and investor reporting solutions to issuers in both the term and warehouse securitization markets. More than 150 organizations across the globe leverage Moody’s Analytics solutions to produce their borrowing base, settlement statements and investor reports. Leveraging this existing technology, Moody’s Analytics launched Lender to not only ensure that lending facility providers would be able to collect, normalize, transform, and validate data from their borrowers, but also be in a position to fully reconcile borrowing base calculations, understand risks across the portfolio, manage deal documents, and expedite the funding request process. With Lender, lending facility providers can rest assured that they will be well positioned to scale their program while ensuring proper controls and risk management
