A New Regime: The Future of Private Credit and Risk Management Needs

The rapid growth of private credit

What is private credit?

As a form of debt financing provided by non-bank entities, private credit operates outside the traditional banking system. Unlike traditional bond markets, in which debt instruments are issued and traded on public exchanges, private credit entails lending directly to businesses, individuals or projects, often through private funds or specialized lending institutions.

The private credit market encompasses a diverse range of segments, each with distinct characteristics and investment profiles. Notable segments include:

• Corporate lending, which involves direct loans to businesses for the purposes of growth, acquisition or restructuring.
• Asset-backed lending, whereby loans are secured by specific assets such as real estate or equipment.
• Consumer finance, which involves high-volume loan collateral. This includes personal loans, credit card debt and other forms of consumer credit, which are bundled together and sold to private credit funds and investors.

Operationally, the private credit sector shares some similarities with the bond market. Both markets require robust systems for capturing deals and valuations and applying marks, although the respective approaches and challenges differ significantly because of the underlying nature of the assets. The bond market typically features standardized, liquid instruments with transparent pricing and regulatory oversight, whereas private credit is characterized by its bespoke nature, illiquidity and more complex risk assessments.

Another key difference is the availability and granularity of collateral data. The public securitized market restricts access to many detailed loan-level characteristics for investors. By contrast, private credit markets have no such limitation. This allows asset managers and traders to apply more granular analytics and modeling when valuing and assessing risk on their holdings. Moreover, investors who effectively accumulate and track the historical performance of their assets gain a knowledge advantage over the rest of the market.

A broad range of private credit investors exists, including direct lending funds, insurance companies, asset managers, hedge funds and real estate investment trusts (REITs). Institutional approaches to private credit portfolios vary significantly depending on the type of investor, and are driven by differences in regulatory requirements, investment objectives, risk tolerance and liquidity needs. Insurance companies and their asset managers, for example, may focus more on stability, liability matching and regulatory compliance, while sell-side institutions may prioritize market opportunities, liquidity and transactional profits.

Drivers of growth in private credit

Demand for private credit has grown significantly in recent years, driven by several structural and market forces:

• Banking regulatory constraints. Since the global financial crisis, increasing regulation and more stringent capital rules have constrained banks’ ability to take on credit risk. Private credit has grown to fill this lending void, particularly for non-standard risks.
• Greater flexibility. Lending within the banking sector is also hampered by a lack of flexibility in terms of the number and range of structures, terms and conditions (T&Cs) and covenants that can be accommodated. By contrast, the highly customized nature of private credit means that financing solutions can be tailored to the specific needs of borrowers.
• Attractiveness to institutional investors. Large pools of capital have accumulated in the asset management sector, providing an investor base for private credit markets. Institutional investors are increasingly drawn to this asset class due to a combination of factors:
  • The potential for higher yields.
  • Diversification.
  • Access to niche sectors that may not be available in public markets.
  • Tailored investment opportunities.
• Strategic fit with long-term liabilities. In addition, the alignment between insurance liabilities and private credit lending has driven greater investment. Institutional investors often have long-term liabilities, such as pensions and annuities obligations, that align well with the illiquid nature of private credit and the long-term nature of private credit lending. The ability to lock in higher returns over an extended period without the pressure of short-term market fluctuations makes private credit a good strategic fit for investors’ goals.

Future market growth

The rapid growth seen in the private credit market in recent years is expected to continue. Chartis currently sizes the market at almost $7 trillion in assets globally and believes that over the next decade it will double in size.

This increase will be driven by geographic expansion, particularly in Europe and Asia, where private credit is currently growing. The regulatory, legal and operational environment has not yet reached the level of maturity of that in the US. Nevertheless, private credit aligns well with domestic market structures in certain geographies, meeting the need for long-term investments and more flexible company-specific or asset-specific lending.

Chartis also believes that demand for asset-backed and consumer-based private credit will continue to grow with firms and individuals that may not have access to public markets, or that require more customized loans and want to leverage the flexibility of private credit. Asset-backed lending will continue to appeal to investors looking for lower-risk opportunities in an uncertain economic environment. And the rise of financial technology platforms, meanwhile, will make it easier for consumers and businesses to access these credit options, fueling further growth in the sector.

The challenges of managing private credit risk

As the private credit market continues to expand, the importance of robust risk management practices is becoming increasingly important. As the sector continues to grow rapidly, more diverse and complex assets are being financed, amplifying the potential for exposure to credit risk, market fluctuations and economic downturns.

This paper focuses on some of the key risk management considerations for companies currently participating in, or thinking of entering, the private credit market – particularly asset-backed finance and high-volume consumer loan collateral.

Data challenges

Private credit risk management is particularly challenging due to the illiquid and opaque nature of the market, the bespoke and often complex structure of deals and the lack of standardized pricing and benchmarking. Unlike public markets, where there is abundant data and transparency, private credit transactions often involve privately negotiated terms, making it difficult to assess and compare risks accurately. Private credit also typically involves borrowers with less-established credit histories or higher leverage, further complicating the assessment of risk. As a result, the pricing and valuation of private credit deals is more difficult and often relies on complex and bespoke internal models or external valuations, rather than observable market prices.

The availability of data in private credit is therefore a key challenge. Those who have the data wield the power. While institutions may look at comparable transactions and recent deals, there is limited historical data on defaults, recoveries and other key metrics that firms need to assess risk effectively. Data needs to be collected from multiple sources, before being reconciled and checked to ensure that it is reasonable and reliable.

Compared to public markets, private credit also requires a considerable amount of complex and ad hoc data management at every stage. Stages go from complicated deal capture frameworks through to the document management required to check for highly specialized and structured covenants, and then all the way through to the challenges of where and how to obtain valuations.

To respond to these challenges, cloud solutions that support a single source of data are crucial, particularly when it comes to handling such areas as collateral data and cash flow models. These are explored in more detail below.

By centralizing data in a unified platform, cloud solutions simplify the process by having all the relevant data and analytics in the same place, saving time and reducing the risk of errors in interpreting data. This consistency is also vital when sharing information with third-party valuation firms, ensuring that all parties are working from the same dataset and analysis, and enhancing transparency. As noted above, access to loan-level data is ultimately what makes granular analytics and modeling possible. Within organizations, a single data source streamlines communication and decision-making, simplifying the sharing of analysis between the front and middle offices and aligning investment strategies with risk management protocols. Externally, a single data source also provides a clearer, more reliable view of the underlying assets and cash flows for investors. Investors that have a framework to maintain historical data and apply granular analytics are able to track the historical performance of their assets. At the same time, they also have a better understanding of the market.

Cash flow modeling and the importance of flexible assumptions and scenarios

Cash flow modeling in private credit risk management poses significant challenges due to the inherent uncertainty and variability in predicting future cash flows from borrowers. Bespoke structures with varying cash flows, repayment terms and interest rates make cash flow forecasting complex. This is further complicated by a lack of historical data and limited transparency into a borrower’s financial health. External factors such as economic downturns, industry-specific risks and changes in market conditions can also drastically impact borrowers, leading to the potential underestimation of risks and unexpected losses.

The unique and often complex nature of the assets and lending structures involved in private credit make it difficult to fit into the rigid frameworks of traditional risk algorithms. These algorithms typically rely on historical data and fixed statistical structures – such as predefined distributions, correlations and assumptions about market behavior – that work well in more standardized and liquid markets. However, in the context of private credit, these rigid structures may not fully capture any nuances.

Firms therefore need considerable flexibility and customized approaches to risk modeling, so they can assess and manage private credit risk more accurately. Most of the parameters for calculating elements such as private credit pricing structures, correlations and volatility are both nonlinear and/or multivariable. There is a need for more dynamic statistical methods that can adapt to the specific circumstances of each loan or asset, and which reflect the complexity and interdependence of the factors that can influence private credit risk.

Regulatory considerations

These are also important considerations within private credit risk management, particularly given the involvement and funding of insurance companies. For insurance firms, the regulatory environment strongly influences how private credit is analyzed, with a focus on capital charges and impacts on solvency ratios. Private credit assets need to be considered within the regulatory requirements for stress testing and scenario analysis, which can involve hundreds of cash flow scenarios.

Normally, asset managers and hedge funds are not accustomed to the requirements of highly regulated institutions. However, institutions investing on behalf of insurance companies are having to meet these client demands. Rather than being able to rely on spreadsheets, for example, they may have to deal with expectations that commercial software is being used for these calculations and analyses.

Surveillance and monitoring

The nature of private credit markets can hamper the surveillance and monitoring of private credit risk. Monitoring private credit risk often requires significant resources, including specialized personnel and technology, to track borrower performance, compliance with covenants and market conditions.

These challenges necessitate robust risk management frameworks that include enhanced due diligence, ongoing monitoring and the use of advanced data analytics to understand and mitigate the risks associated with private credit.

How AI can help firms manage private credit portfolio risk

The complexity of private credit risk means that portfolio management is a critical consideration for firms, particularly when it comes to ensuring diversification, with sectoral, institutional and structural spread. Institutions may feel they are diversifying, but if they hold portfolios of subprime auto loans, unsecured consumer loans and student loans, they may in fact have deep consumer exposure. Being able to look at these broad credit risks across investments is critical.

Another dimension of managing portfolio risk is covenant management. There are signs of increasing maturity in this sector and the beginning of some level of cohesion. Nevertheless, the sector is moving away from a seemingly infinite range of structures, yet still retains much more flexibility than that seen in many other areas.

In this environment, Chartis believes that leveraging highly non-linear statistical techniques, such as machine learning (ML) frameworks and artificial intelligence (AI), can be of significant benefit in improving risk management and operational efficiencies. Benefits include:

• Increasing automation. GenAI tools and models can automate processes and increase productivity by, for example:
  • Automating cash flow rules engines.
  • Extracting covenants from documents and other sources to enable risk and portfolio managers to surveil and monitor risks.
• Improving pricing and valuation. These tools can significantly enhance the development of price histories, the detection of anomalies and the construction of pricing and spread curves.
• Enhancing portfolio management. ML and AI solutions can also play an important role in managing portfolios of private credit instruments, given the extreme degree of nonlinearity and multivariance involved. They can support portfolio management by identifying appropriate clusters of instruments and adding constraints.
• Generating alpha. Firms can use ML and AI tools to generate alpha, by having a process in place to earn better returns on an investment above a benchmark. At the same time, they can mitigate the corresponding risk by:
  • Identifying opportunities by efficiently constructing credit covenants, deal structures and scenarios.
  • Selecting specific portfolios based on how the macro economy or the interest rate environment are changing.

Conclusion

Unlike traditional lending, private credit often involves bespoke and complex financing solutions that require a deep understanding of borrower profiles, collateral quality and industry-specific risks. Effective risk management is essential not only to protect the interests of investors but also to ensure the long-term stability of the market. As private credit becomes a more significant component of the financial ecosystem, the ability to assess and mitigate risks accurately will be a key determinant of success for lenders and investors alike.

Traditional models often fail to capture the true risk in private credit. More sophisticated, variable approaches, enhanced by ML and AI, are required to account for the complex, nonlinear and multivariable nature of the sector. In the diverse, intricate and rapidly growing landscape of private credit, flexible statistical structures will enable more accurate and responsive risk assessment.