Private credit has emerged as a distinct asset class over the past two decades, filling the financing gaps left by the retreat of traditional banks. Unlike conventional bank loans, private credit transactions occur outside the regulated banking system, between lenders and borrowers who negotiate directly or through specialized funds. This structural difference fundamentally alters how risk must be assessed, monitored, and managed.
The Limitations of Traditional Models
Traditional credit risk models, such as consumer credit scores and commercial ratings, were developed based on data from public and standardized markets. They assume the existence of uniform documentation, market comparables, and large-scale historical loss data. Private credit, however, lacks all of these characteristics.
Each private credit transaction is unique, with bilaterally negotiated structures, collateral, and covenants. No two credit agreements are identical. The lender’s advantage lies precisely in this customization—the ability to accurately price risk—but this same customization requires an analytical rigor that standardized models cannot provide.
| Feature | Public Markets (Bonds, Syndicated Loans) | Private Credit Markets |
| Standardization | High (standardized documents, public ratings) | Low (bespoke, negotiated terms) |
| Data Availability | High (deep historical data, market prices) | Low (private information, limited history) |
| Liquidity | High (active secondary markets) | Low (illiquid, buy-and-hold) |
| Risk Focus | Systematic (market) risk | Idiosyncratic (borrower-specific) risk |
Building a Risk Model for Private Credit
Risk modeling in private credit begins where traditional credit scores end. The most effective models combine borrower-specific factors with the characteristics of the transaction structure.
1. Cash Flow Analysis (with a caveat)
Cash flow analysis is the foundation, but with a crucial difference. Unlike the traditional banking system, where historical cash flow trends predict future performance, private credit models must incorporate cash flow volatility, seasonality patterns, and the relationship between operational performance and covenant compliance.
2. Quantifying Covenant Cushion
One of the most predictive indicators of default in private credit is the “covenant cushion”—the difference between a company’s actual financial metric (e.g., leverage) and the maximum limit allowed by the loan agreement. A robust model not only measures leverage but also the company’s proximity to breaching a covenant, as this signals imminent financial stress.
3. Structural Seniority Analysis
The loan’s position in the borrower’s capital structure is a critical factor. A first-lien secured loan has very different loss expectations than a subordinated credit line. Private credit models must incorporate the structural position through recovery rate assumptions that differ from traditional banking models, which apply historical rates by collateral type. In private credit, recovery depends on the specific dynamics of each transaction, including negotiations between creditors and the quality of the collateral.
Continuous Monitoring and Active Management
Due to the lack of liquidity and the idiosyncratic nature of private credit investments, continuous monitoring is even more critical than in public markets. Risk management does not end with the loan underwriting; it is an ongoing process that involves:
•Periodic Reviews: Quarterly or monthly analysis of the borrower’s financial reports.
•Stress Tests: Simulating the impact of adverse macroeconomic scenarios on the portfolio.
•Management Communication: Maintaining an open dialogue with the borrower’s management team to identify potential problems before they become crises.
•Covenant Monitoring: Rigorous tracking of compliance with all contractual clauses.
The Advantage of Specialization
The Role of Technology
While traditional models fail, technology is playing an increasingly important role in private credit risk assessment. New platforms are emerging that use machine learning and alternative data sources to provide a more holistic view of borrower risk.
Alternative Data Sources
•Bank Transaction Data: Analyzing a company’s bank transactions can provide real-time insights into its financial health, such as revenue trends, customer concentration, and cash flow volatility.
•Industry-Specific Data: For example, in the transportation industry, data from GPS tracking devices can be used to monitor the utilization of a company’s fleet.
•Supply Chain Data: Analyzing a company’s supply chain can help to identify potential risks, such as dependence on a single supplier or exposure to geopolitical events.
Machine Learning Models
Machine learning models can be trained on these alternative data sources to identify complex, non-linear patterns that are not captured by traditional models. For example, a machine learning model might be able to identify that a company’s revenue is highly correlated with a particular macroeconomic indicator, or that a certain pattern of bank transactions is a leading indicator of default.
The Human Element
Despite the advances in technology, the human element remains crucial in private credit risk assessment. No model can replace the experience and judgment of a seasoned credit analyst. The role of the analyst is to interpret the outputs of the models, to conduct due diligence on the borrower and its management team, and to structure the transaction in a way that protects the lender’s interests.
Conclusion
The failure of traditional credit models in the private credit environment highlights the need for a more sophisticated and nuanced approach to risk assessment. By combining deep industry expertise, rigorous financial analysis, and the power of new technologies, private credit managers can effectively price risk, structure robust transactions, and generate attractive returns for their investors. In a world of low interest rates and volatile public markets, private credit offers a compelling alternative for those who are willing to do the hard work of fundamental credit work that this asset class demands.