Credit Underwriting: Pre- and Post-COVID

7 min read

Executive Summary

The economic crisis driven by COVID-19 has exposed shortcomings in existing methods of underwriting and risk analysis in business lending. A major transition in post-COVID underwriting is being driven by advanced technology and a focus on real-time analysis.

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The U.S. and global economies suffered a rapid and devastating shock from the COVID-19 pandemic. Historic increases in unemployment and drops in personal consumption happened seemingly overnight as lockdowns were declared and businesses of all types and sizes confronted unprecedented circumstances. 

The economic ripple effects have spread far and wide, putting pressure on the financial sector where banks and financial institutions face unique uncertainties that directly affect how and when they make loans and credit available to borrowers. 

As a result, lenders have been forced to make adjustments on-the-fly to revise their methods and standards for underwriting. Underwriting, which is the process of evaluating a borrower’s creditworthiness, is essential to managing risk in a lending portfolio, but many traditional underwriting methods are ineffective in a post-COVID economic climate.  

At the same time, many small- and medium-sized enterprises (SMEs), which have been hit hard by lockdowns and reduced economic activity, need financing to cover expenses and stay afloat until the economy recovers. As borrowers, these companies face a new lending environment shaped by major shifts in underwriting. 

As the COVID-19 recession is likely to leave lasting scars with continuing uncertainty that could go on for months or years, reviewing the dynamics of post-COVID underwriting is critical to understanding the current landscape of business credit and lending and how it will change in the future. 

What is Underwriting? 

Underwriting is the process that a bank or financial institution uses to determine whether a borrower is creditworthy. As a fundamental tool for managing risk, it enables lending and credit. The greater the risk assigned to a potential borrower during underwriting, the higher the chances they will be denied financing, required to pay a higher interest rate, or compelled to meet more stringent borrowing terms. 

How Has COVID-19 Changed Underwriting?

Traditionally, underwriting has been based primarily on historical information. A borrower’s past tax returns and credit history are prime considerations in typical underwriting. In business lending, underwriting could also evaluate the historical performance of the business’s sector of the economy. While the exact data sources vary, a basic principle in underwriting has held that past performance could predict future credit risk. 

COVID-19 and the sudden shock it inflicted across the economy has eroded the value of many of these traditional underwriting methods. Historical data becomes less applicable when economic circumstances change so drastically and rapidly. Prior year tax returns or industry projections lose salience in the face of such a profound crisis. 

Additional uncertainty in underwriting stems from factors unique to COVID-19 and its economic effects:

  • The epidemiological curve: The evolution of the virus depends on a multitude of factors, some of which are still up for scientific debate, but the epidemiological situation directly impacts the pace of re-opening and possibilities for future lockdowns and business closures. 
  • Government intervention: The political environment is challenging, making it hard to know whether new rounds of economic stimulus will be approved to support lenders, businesses, and/or consumers. In addition, the effects of past stimulus programs could mask underlying economic conditions and credit risks. 
  • Geographic variation: Local and regional variations in economic impacts complicate the analysis of industries using national data. Localized outbreaks and political responses in different geographic areas generate further uncertainty. 
  • Diverse economic effects across sectors: Economic impacts are not uniform across sectors. For example, restaurants that have been able to quickly pivot to take-out and delivery have distinct financial prospects from those that rely only on in-person dining. 
  • A worldwide problem: The international nature of the pandemic means that recovery, both for the economy as a whole and for specific businesses, may be susceptible to external risk even if local or national circumstances improve. 

Uncertainty is always present in underwriting, but the post-COVID inability to rely on historical, backward-looking information has pushed financial institutions to adopt new approaches to evaluating creditworthiness. 

How Are Lenders Responding to a New Underwriting Environment? 

A natural reaction for many lenders facing an uncertain economic climate is to tighten lending standards. In a recent survey conducted by the Federal Reserve, senior loan officers reported that lending standards are the tightest they have been since 2005. Tougher criteria for borrowers are accompanied by stricter loan and credit limits, loan-to-value ratios, and repayment terms. 

However, many financial institutions realize that an overly cautious approach could cause them to miss out on valuable business opportunities. For that reason, they are working to modernize how they assess creditworthiness. Central to these changes is turning underwriting’s attention to real-time risk analysis. 

Lenders are increasingly looking to present-day financial statements instead of relying on historical data like tax returns. New borrowers may be required to provide this information more frequently and undergo extra credit checks. Key financial information is subject to double- and triple-checking in the leadup to closing. Existing borrowers may also receive added scrutiny, and when updated indicators are worrying, lenders may offer relief programs or forbearance to reduce defaults and charge-offs. 

Technology is also enabling a major shift in post-COVID underwriting. For example, machine learning platforms can scour huge and diverse datasets, applying advanced algorithms to identify factors driving real-time credit risk. These technologies can provide a granular view of a specific business and its cash flow and financial health. In addition, they can be deployed for higher-level insights about economic risk across different geographic areas and industries. 

Incorporating machine learning into underwriting not only makes credit evaluations more robust and up-to-date but also allows more of the process to be automated. Simplifying underwriting reduces costs for lenders and gives borrowers quicker access to mission-critical financing. 

This changing landscape has created opportunities for new players to enter the lending market by harnessing modernized methods of underwriting. For example, Kabbage, a startup with an automated platform for small business financing, initially restricted its normal lending in response to COVID-19 but then adjusted by using its platform to streamline application and approval for the Paycheck Protection Program (PPP). Fundbox, another alternative lender, tightened standards at first but gradually expanded its portfolio based on algorithm-driven risk analysis. Fundbox closed a funding round in late May, and Kabbage was acquired by American Express in August, demonstrating that investors see these types of solutions as part of the future of business financing.

In addition to startups, major technology companies like Amazon and Apple have ramped up programs for lending and financing. Amazon Lending provides short-term capital to small businesses that utilize the Amazon Marketplace, and Apple Financial Services offers flexible financing plans for technology purchases. 

From startups to Big Tech, these developments reflect how underwriting is evolving and moving beyond the walls of conventional banks. Companies with capabilities to do real-time, high-level data science are increasingly taking advantage of opportunities to build out in-house lending programs. 

Future Directions for Post-COVID Lending and Underwriting

In the face of the COVID-19 crisis, underwriting is in a period of transition and modernization. While no one knows exactly how the pandemic or economic recovery will unfold, there are clear signs that underwriting has undergone an important transformation. 

Historical data will continue to factor into lending decisions, but real-time credit evaluation is here to stay, especially because technology makes it easier and more insightful. At the same time, a broad consideration of industrial sectors will continue to be augmented by a focus on subsectors and individual borrowers.

Innovative financial technology enables robust data analysis that can’t be conducted manually, and technology platforms that take advantage of cutting-edge data science are what make these new approaches to underwriting scalable. COVID-19 has revealed the value of fine-tuned machine learning solutions that are automated, built for real-time analysis, and adept at determining business-specific risk in dynamic situations. Uncertainty will always exist for lenders and financial institutions, but the current crisis has plainly demonstrated that advanced technology has a vital role to play in underwriting and risk management. 

About the author

Kathryn Rungrueng

Written by Kathryn Rungrueng

The Tillful team is writing articles to help companies grow their credit and improve their business financial health. If there’s a topic you’d be interested in learning about, let us know at

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