Direct lenders are fundamentally in the business of credit risk management. Over the past few decades, lenders have built powerful processes and technologies for evaluating and mitigating the credit risk of potential borrowers.
Lenders are now realizing risk management practices can be expanded and augmented by using data to place credit decisions in the context of an investment portfolio. Portfolio-level risk management practices have become table stakes for institutional investors in public debt and equity, but a lack of equivalent data for private companies has prevented the same practices from being implemented by middle market lenders.
For financial data to be useful to lenders, it must be processed and structured in a manner allowing lenders to evaluate and respond to real-time portfolio risk exposures.
Credit Risk Management in Theory
Credit risk is the possibility of financial loss resulting from a borrower’s failure to repay principal and/or interest. Effective credit risk management is at the core of successful lending practices and should influence every aspect of a lender’s operations, including sales, underwriting, and portfolio monitoring.
A lender’s credit risk management process should involve:
- Identifying credit risk exposures
- Establishing exposure limits
- Exposure monitoring and, when necessary, exposure adjustment
Yet, today, the majority of credit risk assessment and management is performed at the level of an individual borrower. Many lenders employ extensive and sophisticated analysis of debt capacity, deal structure, loss estimation, and more for each potential deal. The problem is that restricting credit analysis to individual borrowers misses valuable information that can only be gleaned at the portfolio level.
Contextualized Credit Analysis
The next generation of credit leaders will understand and evaluate lending decisions in the context of a broader credit portfolio.
To illustrate this principle, let’s use a simple example. Suppose you’re considering a loan to two companies that operate in the hospitality and consumer staples industries, Joe’s Hotels and Jane’s Groceries.
The companies have identical risk profiles, expected return, and deal structure.
What is the most effective way to deploy your capital?
Traditionally, the answer to this question involves an in-depth analysis of credit quality, market conditions, deal structure, and more. Opportunities with the optimal Internal Rate of Return (IRR) and risk profile are then brought to the credit committee for approval.
Lenders have mastered this part of capital allocation decisions, but these decisions don’t occur in a vacuum.
In an ideal world, a credit analyst would consider their organization’s existing risk profile and tolerance, if only they had access to the data.
For example, if 70 percent of outstanding debt is issued to companies in the Hospitality industry, are you comfortable with further increasing that exposure? What about at 45 percent?
The answer also changes over time. Lenders may have been happy to lend to hospitality borrowers one month ago. During the COVID-19 crisis, risk exposures needed to be re-evaluated.
The decision is relatively simple when limiting risk factors to hospitality vs. consumer staples. In practice, however, the decisions are more complex. Risk factors span industries, geographies, product types, collateral, and more.
In the past, technological limitations have made portfolio-level risk management challenging. New technologies are empowering lenders to institute more advanced risk management techniques.
Credit Risk Management in Practice
Risk management practices for publicly traded debt and equity portfolios have evolved extensively over the past few decades. In fact, these practices are taught as a required, standalone class in nearly every university-level finance curriculum. But until now, direct lenders haven’t had access to these tools. The reason for this is simple: lack of financial performance data.
To take advantage of effective risk management practices, lenders must be able to assess and act upon as close to real-time portfolio risk exposures as possible.
Assessing risk in real time is challenging because organizations need a centralized data source in order to determine:
- Overall risk in the portfolio
- Overall risk in your deal pipeline
- Mitigation and remediation steps likely to be necessary
Your chief risk officer and other leadership should be able to view and act upon this information on a daily basis. Credit committees should have access to this information during the process of approving any new loan. Credit teams should take this one step further by being able to dynamically identify “windows” of risk that the portfolio should fill at any given time and to instruct their sales teams accordingly.
Today, this information is difficult to compile because of legacy data management strategies and technologies. In many cases, borrower financial information lives in individual Excel spreadsheets or rigid databases, making it challenging to share and aggregate.
You need a solution that unlocks your data and makes it actionable. This means:
- All portfolio information can be viewed anytime in a clear, consistent format
- You have the ability to slice and dice the data based on financial indicators, industry, geography, or other criteria needed for your risk officers to quickly and reliably assess risk
Let’s Model This Out
To implement effective modern credit risk management practices, an organization needs to collect consistent, accessible, unified financial data.
Data collection and management begins with a data model. A data model is a way of organizing and standardizing information about real-world entities.
A common example of a data model in the financial industry—and the most important for private companies—is an income statement. It summarizes information about a company’s profit and loss over a specified period of time. Depending on the economic drivers of income, companies have different data models for organizing profit and loss.
A bank’s income statement will focus on net interest margin, while interest margin is less relevant for a retail company. Just as a company chooses a data model to create its financial statements for its own purposes, lenders create data models to represent information about borrowers.
Consistent
The core of a good data model is consistency, meaning all data is produced by a regulated, predictable process. Without it, you’re building the foundations of your knowledge on sand. In the credit risk management process, financial spreading is the key to creating a consistent data model, because spreading transforms raw financial statements into consistent useful data.
Let’s return to the example of Joe’s Hotels and Jane’s Groceries. On the companies’ respective balance sheet, Joe’s Hotels reports a value of $2 million for “Hotel Properties”, while Jane’s Groceries reports a value of $1.5 million for “Stores”. In comparing these two businesses, credit analysts need to standardize these highly specific items. Both “Hotel Properties” and “Stores” constitute property, plant, and equipment assets for these businesses. A consistent process for spreading these financial line items allows credit analysts to create useful data for comparison.
Accessible
Armed with consistent data, an organization must make it accessible to key decision makers. Too much valuable data today is hidden in a multiplicity of spreadsheets and individual loan documents, rendering it useless to lenders.
If the underwriting team is structuring a deal for a New England consumer staples company, they should have access to standardized financial statements collected from similar companies over the past three years. If the sales team is targeting upsell opportunities, they should have access to up-to-date information collected by the portfolio monitoring team, so they can recommend the best products to their customers.
Unified
Finally, an organization must have standardized business rules to produce unified data. To implement portfolio risk management, key decision makers need a portfolio view of standardized data.
It’s impossible to manage portfolio credit risk by examining each individual borrower in a bubble. An organization needs to decide what credit risk metrics are important and implement processes to view these in aggregate.
For example, debt service coverage ratio (DSCR) is a common metric used to analyze credit risk. In addition to viewing DSCR for each borrower, there should be rules for combining data for portfolio level risk analysis. Aggregated DSCR allows an organization to answer questions like:
- What is the average DSCR across all borrowers?
- Has the average DSCR increased from last quarter?
- What is the distribution of DSCR across all borrowers?
Consistent, accessible, unified data allows an organization to answer these questions instantly. Empowered with real-time information, employees from credit analysts to the chief risk officer can optimize credit allocation and return on capital in a more responsive way.
For example, seeing that portfolio-level DSCR distribution has shifted downward/upward due to market factors, origination teams could pursue more conservative deals to correct that imbalance, or vice versa if DSCR shifts in the opposite direction, a more aggressive acquisition strategy could be pursued. In both cases, a nimble business could use the desired quality to define target markets and accounts for new sales and marketing campaigns. The end result is a tighter, more proactive organization that responds to changing market conditions in the context of the risk exposure in the existing business.
Summary
Using contextualized credit analysis, lenders can become more effective and profitable. For credit risk information to be actionable, lenders must have real-time financial information for every company in their portfolio, both as individual borrowers and in aggregate.
Consistent, accessible, and unified financial data is the cornerstone of a responsive risk management approach. Investing in technology to enable this new capability will deliver compounding return on investment as your internal data asset expands and enriches your business processes across teams.
To learn more about how you can get started today, visit our platform page, here. 
Editor's note: This blog was previously published by Fincura on April 6, 2020. It was updated on July 19, 2022. Fincura was acquired by Numerated in December of 2021 and now powers Numerated’s financial spreading and analysis solution.
