The growth of private markets over the past two decades has been nothing short of remarkable, as businesses of all sizes increasingly turn to non-traditional sources of financing to meet their capital needs. Indeed, according to data provided by Preqin, private credit as an asset class has witnessed a 10-fold increase in AUM since just before the Great Recession till today, having now reached an estimated $1.5 trillion. Preqin also forecasts continued growth in the sector, achieving $2.3 trillion by 2027…which may actually be conservative. This expansion has been driven by a confluence of factors, including regulatory changes, the de-risking by traditional lenders, the search for yield, and the evolving financial landscape.
As private credit continues to carve out a substantial presence in the broader financial ecosystem, the need for contemporary technology platforms to support and enhance these lending activities is increasingly clear. Such platforms can provide the infrastructure and tools necessary to facilitate efficient and data-driven lending, offering borrowers, lenders, and investors the agility, insights, scale, and risk management required to thrive in a rapidly evolving landscape. Here, we explore the growth in private credit markets and consider the essential role that technology plays in shaping its future.
THE GROWTH OF PRIVATE CREDIT AMIDST A CHANGING REGULATORY LANDSCAPE
In the wake of the 2008 financial crisis, regulatory authorities worldwide embarked on a mission to fortify the financial system against systemic risks. Stringent regulations, such as the Basel III framework, Dodd-Frank Act, Volcker Rule, and various regional directives were introduced to bolster the capital adequacy, liquidity, and risk management practices of banks. Under Basel, higher risk-weighted assets, like leveraged loans, understandably require higher capital reserve ratios, making such assets more expensive to carry on a bank’s balance sheet. While these measures are necessary to enhance stability of the global financial system, they also impose significant constraints on bank lending capabilities, especially for mid-market and SMB borrowers.
Then, in July of this year the three U.S. federal banking regulators – the Federal Reserve, FDIC, and OCC – proposed a set of enhanced Basel standards, collectively referred to as Basel III Endgame (B3E). The proposal is currently subject to an extended 120-day comment period ending on November 30, and is essentially the U.S. adaptation of the 2017 revisions to the Basel III capital regime created by the Basel Committee on Banking Supervision.
B3E seeks to impose uniform credit and operational risk models on banks, along with greater oversight of internal market risk models that may ultimately face similar regulatory standardization, though not immediately. Most significantly, these proposals expand the regulatory capital perimeter to include all banks with at least $100 billion in assets, a considerable departure from the previous framework covering internationally active banks and those with at least $700 billion. Additionally, banks with assets of $100 billion or more would include unrealized gains and losses in their available-for-sale (AFS) securities when calculating their regulatory capital. The tougher stance on capital requirements and expansion of coverage is a direct response to more recent market events, including the failure of some regional institutions, like Silicon Valley Bank, Signature Bank, and First Republic earlier in the year.
These proposals acknowledge that higher capital requirements could lead to behavioral changes in the banking sector and suggest a phase-in period to allow affected institutions to generate the required capital through retained earnings over two years. These changes, coupled with shifting monetary policy, may reshape the U.S. banking landscape, potentially prompting balance-sheet contraction and greater consolidation, particularly amongst smaller “large” banks now in scope of stricter regulatory standards.
Collectively, this has been the backdrop to a marked expansion in private credit markets and the shadow banking system, at large. Fueled by these regulatory changes and tighter lending standards, non-bank financial institutions (NBFIs) have stepped in to enhance liquidity and provide access to capital that may be unavailable or less attractive from traditional funding sources. These NBFIs – including private credit managers, direct lenders, REITs, Business Development Companies (BDCs), private equity managers, and other participants – can often provide greater agility, speed, industry expertise, and customized terms and covenants to borrowers than more rigid bank underwriting processes may allow.
NAVIGATING THE CHALLENGES AND OPPORTUNITIES IN TODAY’S PRIVATE CREDIT LANDSCAPE
In the current high inflationary and rising rate environment, private credit can offer investors reduced market correlation, along with stronger structural protections afforded by robust due diligence, loan covenants, and higher call premiums. The asset class also provides higher yields and lower interest rate risk, since most loans utilize floating rates with short reset periods, resulting in lower volatility and duration. Securitization through the issuance and management of Collateralized Loan Obligations (CLOs) also serves to enhance liquidity by moving loans off an underwriter’s balance sheet, generating cash and freeing capital for new loans. Investors benefit from the greater diversification of an underlying loan portfolio and the ability to target preferred risk-adjusted returns based on the seniority and claim priority of the tranches in which they invest.
All of this has increased demand for private credit origination, structuring, servicing, and investment. This has not only led to organic growth in the sector amongst established managers, but also a flurry of new entrants. Too much dry powder, however, can reduce spreads and drive undisciplined managers to chase less credit-worthy deals. In a stable environment, return parity amongst managers may be largely uncorrelated to their capabilities, as market beta is the dominant factor. A rising tide lifts all boats.
The macro headwinds in the current high inflationary, rising rate, and potentially recessionary environment, however, pose considerable risks for ill-prepared or more nascent managers. In the current environment, credit risk is on the rise. Defaults are likely to increase. Managers will be tested. This is where alpha is clearly differentiated and likely to widen the return dispersion between the “haves” and the “have nots”. Effective management in private credit is, after all, as much about loss mitigation as it is about high-yielding deal origination. This puts a manager’s platform, risk management, and technology front and center, and investors should strongly evaluate these capabilities when conducting due diligence on manager selection.
So, how are managers handling these challenges? The short answer is: it’s too early to tell. It’s no secret that many private credit managers have historically been very document-heavy and have relied on manual workflows, spreadsheets, and email-centric processes across their investment and servicing functions. And there’s often been little incentive to shore up client service, as relationships and performance have historically been sufficient to maintain investor commitments amongst LPs. But as the industry expands, as deal sourcing becomes more data-driven, as market conditions gyrate, and as the demographics and expectations of investors evolve, it seems unlikely that the same cloistered approach will continue to dominate.
THE CRUCIAL ROLE OF DATA IN PRIVATE CREDIT PLATFORMS
Data is at the core of any effective and scalable credit platform, whether public or private.
Participants in the public markets have already begun to adapt more robust data and analytic capabilities, particularly in their investment processes, albeit with varying levels of sophistication and maturity. And while the application of data and data science in public markets may seem more achievable, given the sheer volume, velocity, and veracity of available data sets, plenty of opportunities exist for private market participants to similarly exploit these capabilities.
When I first joined Blackrock in 1990, the firm was a nascent asset manager almost exclusively focused on U.S. domestic fixed-income with a high concentration in ABS/MBS and associated structured products, like CMOs. As with any asset-backed product, the ability to dive into the underlying loan pools is critical for both the initial investment decision and subsequent risk management. Structured products introduce additional requirements and complexity. To properly analyze these assets, a bottoms-up approach is required to derive cashflows generated by the underlying collateral across various economic scenarios, which are then applied to the entire deal structure…even if you only own a single tranche. Trading these assets on a CUSIP-only basis is ineffective, as it fails to capture the dynamism and negative convexity attributable to prepayment risks and other factors. While Blackrock was committed to such disciplines, many less sophisticated investors in those days were not, resulting in more than a few high-profile bankruptcies during those early years.
Private credit and CLOs have similar, if not more demanding requirements. The ability to dive into loan-level details remains critical, but unlike the conforming loans collateralizing a residential agency mortgage pool, leveraged loans are more bespoke with customized terms and covenants applied to a diverse set of borrowers. The ability to onboard and monitor the health of loans across a portfolio can therefore be highly complex, as much of the underlying covenant data is unstructured and borrowers may use different financial reporting and accounting methodologies.
These fundamental requirements present several challenges to private credit managers that can only be solved by the presence of a coherent operating model, a holistic data architecture, an integrated data pipeline, and efficient, if not automated workflows. Conversely, the lack of such a platform introduces considerable market, credit, and operational risks to the manager and, ultimately, to investors. It further impedes a manager’s efficiency and ability to drive positive operating leverage if manpower must be ramped up with each new deal.
DATA AND TECHNOLOGY CONSIDERATIONS
Minimally, managers should consider the following:
- A robust data architecture and data pipeline that can capture, normalize, curate, enrich, govern, and distribute a wide range of both financial and non-financial data sources. This includes, but is not limited to transactions, reference data, borrower information, terms, loan documents, covenants, financial statements, credit reports, deal structures, market data, economic data, and alternative data sets. Database selection must also account for the fact that much of this data is unstructured.
- Account and asset database masters that can traverse deal structures, underlying collateral, and the organizational, legal, and capital structures of borrowers. For example, a security master with a recursive, bottoms-up design allows for cashflows from underlying collateral to be rolled up and applied to more complex structures and waterfalls.
- Automated workflows that can streamline borrower due diligence, origination, underwriting, documentation, and servicing to enhance efficiency and reduce operational costs and operational risk.
- An integration fabric that can ensure a seamless flow of data and processes across both in-house and third-party systems, functions, and processes. Too many federated applications and databases without well-defined services, APIs, and orchestration drives inefficiency, cost, and operational risk. Also, as data is increasingly pushed out from central repositories to the edge, APIs allow technically adept end-users, like modelers, to access it in more productive ways, whether using Python, Excel, or other tools.
- Institutional-grade portfolio management and risk tools that support deal origination, execution, modeling, structuring, rebalancing, and ongoing risk monitoring. This seems like a no-brainer, but how many of these processes currently take place on spreadsheets or other end-user applications?
- Advanced analytics, scenario analysis, AI/machine learning, and predictive indicators to assess interest rate, concentration, and credit risks across both loan-level and macro factors. This includes borrower and asset performance; macro and regional economic conditions; sector; geography; COGS; media; sales activity; traffic; and other dimensions. The richness, and diversity of alternative data sets, in particular, represents greenfield opportunity for many managers to derive insights from information that was too difficult to assemble, or simply not available to prior generations of portfolio and risk managers.
- Strong data processes and governance to ensure the quality, timeliness, provenance, cataloguing, security, and tagging of data. This is, in part, a byproduct of data architecture and integration: data without governance leads to heavy exception processing or, worse yet, unidentified errors that can metastasize.
- Rich, self-service portfolio, operational, risk, performance, and investor reporting to provide access to real-time, actionable data.
- Ensuring that the platform can adapt to changes in financial regulations and compliance standards across the different regulatory regimes under which a manager operates.
- Use of public cloud to provide access to scalable, just-in-time compute and storage, allowing an organization to ramp up/down as needs dictate, and to better align infrastructure expense with the ebb and flow of business cycles by shifting from CapEx to OpEx.
What about AI?
AI is clearly a hot topic, and one that portends material socioeconomic change well beyond credit markets. For private credit managers, AI can generate new and timely insights across multifarious data sets that may simply be too large, active, or disperse for a human analyst to comprehend in a reasonable period of time. These insights may reveal critical information about the likelihood of potential investment success and risks. Consider, for example, how demographics, macroeconomic activity, regional employment, consumer practices and patterns, real estate activity, geospatial observations, shipping data, manufacturing practices, and news, amongst many other sources of data, can be leveraged to better inform investment decisions and serve as predictive indicators of both positively, and negatively correlated risks.
There’s also still no getting around the fact that plenty of loan information and financial reporting remains document-centric. Machine learning and Natural Language Processing (NLP) tools can be used to streamline and automate manually-intense loan processing and servicing tasks. Depending on document formats, preprocessing, like the use of optical character recognition (OCR) or computer vision, may be required to first convert documents into digitally readable text.
Unfortunately, you can’t go from zero to AI! A data strategy and architecture are critical enablers. Leadership, vision, talent, investment, and execution are, perhaps, the most important contributing factors to the success or failure of such an undertaking. This is squarely a business endeavor accompanied by well-defined business objectives, milestones, and metrics; it’s not a backroom IT initiative. CEOs that recognize this will rightly drive related discussions and decisions in their respective executive committees and boardrooms. Organizationally, they will also be well-served by positioning their technology, data, and digital leaders as direct lieutenants, rather than as layered under more operationally focused functions.
CONCLUSION
Investment in private credit as an asset class continues to grow. Forward-thinking managers will need to consider their end-to-end digital capabilities as they seek to maintain an edge and drive healthy returns amidst greater risks posed by current economic headwinds and potential dislocations created by new technologies. This is especially true as managers consider these impacts on borrowers. Areas, such as investment selection, investment governance, risk management, operations, and client service will all benefit from a contemporary digital and data strategy, which also creates a foundation for the adoption of AI. Beyond simply nice to have, this has increasingly become existential for participants in the sector. Those that actively, if not aggressively pursue such capabilities will be well-positioned for the future, while those that cling to legacy practices and overly manual processes are apt to become the next casualties of the digital age.
