A data-driven strategy will be key for finance organizations facing an uptick in auto and equipment non-performing loans this year.
Equipment and auto finance organizations rely on risk ratings to manage expected lease and loan defaults and delinquencies. But today’s risk ratings and models are based on pre-COVID-19 numbers, so how applicable will they be in 2021?
Vaccine campaigns have begun globally, which is welcome news, and GDP projections suggest that we’ll see a rebound this year of roughly 5 percent. However, as government stimulus support for businesses ultimately tapers off, lenders and lessors we’ve talked to expect to see an uptick in non-performing loans (NPLs) and repossessed assets, particularly in the second half of the year.
While it’s tempting to look back at the 2007-2009 financial crisis to try to predict which asset classes might be vulnerable, we think the pandemic has created a unique set of challenges. It’s a different time. For instance, in the previous recession, auto loans continued to perform well because people needed to get to work. Now that many people are working from home to comply with social distancing measures, that won’t apply. So auto loans could be more susceptible than usual.
Same thing with office equipment and technology. In the new remote work era, businesses will need to come up with a new plan. Will they expect workers to return to the office, will they allow them to continue working from home or will they use a hybrid approach? These decisions will have a large impact on the management of business loans and leases. What about machinery and equipment in other segments? Should lenders and lessors leave this equipment in place with a new payment plan or request that it’s returned?
Predictive analytics can help lenders and lessors meet the challenges of NPLs head on
To meet the challenges of NPLs this year, lenders and lessors need to develop a data-driven strategy based on more than just existing risk ratings. We believe boosting your analytics capabilities will be key. Predictive analytics could help answer the above questions and more, providing you with an early warning system so you can prioritize personalized client outreach.
This will be especially critical for asset classes that aren’t easy to identify as vulnerable—including those affected by a trickle-down from other classes. For example, think about all the businesses affected by people working from home. It’s easy to see the impact on local restaurants and stores, but businesses directly tied to the offices where people worked pre-COVID are also going to be affected. We expect this trickling-down to impact a wide range of businesses, such as office equipment providers, custodial services, security services and even paper recycling. All of these probably use some form of equipment finance.
A broader view of the current business landscape, based on aggregated data and more sophisticated analytics, could greatly improve all aspects of NPL management.
Turn insights into action
How can you start on this path today? We suggest beginning with a review of your current data sources, both internal and external. Are there any gaps in the information you need to assess risk? To gain a fuller picture, you might need to integrate other sources, including non-conventional credit data such as unemployment and utilities payments data, from third-party providers. Consider the frequency of data updates too. Is the data updated in real time or near real time, daily or monthly? Insights based on out-of-date data won’t help you to make the best data-driven decisions.
The quality of the data is also critical. Some datasets include unidentified biases, such as those influenced by geography, ethnicity or socio-economic status, as well as other errors and inconsistencies. It’s important to be able to evaluate the quality of the data, since the value of analytics insights is only as good as the data they’re based on. One option is to work with a consultancy like Accenture that can recommend vetted data vendors and help you integrate, match and link the data with your internal sources.
Review your existing models and risk assessments to predict which segments or asset classes are most likely to be affected by NPLs. The goal is to mine the billions of data points you have available to identify the appropriate signals for COVID-related stress. If you’re not already using artificial intelligence and machine learning, and a cloud-based platform, it’s time to deploy these tools to gain better and quicker insights at scale.
Now is the time to get started
A return to “normal” may require a longer period of managing customers at risk than we originally expected. Success will demand closer customer monitoring and efficient operations. Lenders and lessors that ramp up their analytics capabilities now and take advantage of the wealth of credit and non-conventional data available will be in a better position to manage the full spectrum of NPL prevention and delinquency management. They’ll also be more resilient in the face of new challenges in the future.
If you don’t have the skills or capacity to implement these tools and strategies in-house, Accenture can help you with an end-to-end solution to improve your analytics, fix inefficiencies in your current execution of NPLs, streamline your technology, and upskill or reskill your workforce to manage loan defaults and distressed clients more efficiently. To learn more, read the PDF: NPL Prevention and Default Management.
Do you have questions or comments about how analytics can help you prepare for the expected uptick in loan defaults and delinquencies in the auto and equipment landscape? Get in touch.