Unmanned Aircraft Systems (UAS) have been experiencing robust growth in the U.S. national airspace system the past few years. By the end of 2020, more than 488,000 commercial UAS have been registered with the FAA since registration opened April 1, 2016, and during 2020 the fleet has grown on average by 7,850 units per month. In its Aerospace Forecast for Fiscal Years 2021-2041, the FAA predicts that the commercial UAS sector is at an inflexion point, demonstrating powerful stages of growth ahead. As the FAA continues to clarify operating rules under Part 107 of the FAR, which governs commercial operations of UAS, the UAS fleet will continue to expand. The FAA forecasts that the UAS fleet will grow to as much as 1,144,000 units by 2025.
As the size of the UAS fleet grows, so do the capabilities of UAS airframes, with a concomitant increase in the acquisition cost of UAS airframes and payload packages. Whereas acquisition costs in 2016 averaged less than $3,000 for a reasonably capable UAS, today a single multirotor UAS and sophisticated Lidar payload package can approach $75,000 to $100,000 in cost. And this expense does not include necessary support equipment such as transport vehicles and launch facilities. With this increase in acquisition cost comes increased needs for commercial UAS operators to resort to equipment financing to obtain the equipment necessary to meet demand for their commercial UAS services. Whereas previously an operator could cash flow four or five UAS airframes at an acquisition cost of $3,000 per airframe, fielding a fleet of UAS costing upwards of $100,000 per airframe present an entirely different and much more economically challenging proposition.
This growth therefore also presents opportunities and challenges for equipment lessors and financers. The dynamic nature of UAS growth provides many new opportunities to lessors to grow market share in a cutting edge, highly technical industry at its inception; after all, Part 107 which makes the industry viable only became final April 1, 2016. On the flip side, this fact also presents a unique challenge to lessors. How does a lessor competently vet the credit worthiness of a potential lessee looking to operate a commercial UAS business in an industry only a little over five years in existence? And how does a lessor satisfy its own funding sources that the collateral is economically attractive from profitability and risk perspectives? To date, there is virtually no history from an equipment leasing point of view to turn to. One method that provides a useful means of assessing profitability and risks associated with equipment leasing for commercial UAS is credit scoring. Combining traditional credit factors with those particularly related to commercial UAS operations provides a means for a lessor to adequately rate its credit risk.
Credit Scoring
Credit scoring is one of the leasing industry’s most important risk management tools. Scoring is a system used to analyze data on potential lessees to predict their future performance. Used properly, scoring systems provide credit managers with an enormous amount of statistical information they can use to control their portfolios with high predictability.
Given the likely size of UAS lease transactions, credit scoring is particularly appropriate. Credit scoring models are used 3.4 times more frequently to aid in the evaluation of transactions up to $250,000 than for larger transactions, and 89.5% use some type of auto decisioning either for approval or decline.
Benefits and Limitations of Scoring
Credit scoring has a number of benefits. Scoring assists a lessor with regulatory compliance in that, when properly implemented, it is consistently applied and does not consider prohibited characteristics. In the commercial UAS sector, the most likely model will consist of an application system, which draws relevant factors from a potential lessee’s application, combined with data available from credit bureaus and other data such as demographic clusters and census tract median income to score credit. Such systems offer additional benefits, including objective risk evaluations, cost-efficient processing where unqualified applicants are quickly eliminated and highly qualified applicants are speedily approved, statistical control of portfolios and controlled experimentation. With the ability to array the portfolio, experiments can be run to accept more high-risk but potentially more profitable accounts, offer larger lines and so on.
Drawbacks of scoring include the cost and time associated with the development of the model. Limited predictability is also an issue. A scoring system can’t identify individual good or bad accounts, only the odds that an account will be good or bad. If the odds are 100 to one an account will be good, acceptance is easy. Less so if the odds are five to one. Finally, scoring systems degrade with time as the population and economy change. They must be continuously monitored and validated to determine how well they perform.
Traditional Scoring Factors
An effective scoring model for commercial UAS leasing will include the traditional factors. Items such as age, own or rent, occupation, telephone, income, years on job, prior job and years on prior job, dependents, credit references, bank references, credit outstanding and debt burden all would come from an application. Sample data from credit bureaus would include time in file, number of satisfactory payments and delinquencies, length of time since last delinquency, number and balance of charged off accounts, number of inquiries and length of time since last inquiry, age of trade accounts, total credit limits on revolving accounts, utilization of revolving accounts, total balance owing as a percent of highest outstanding for various types of loans, and number and types of trade recently opened.
Commercial UAS Specific Factors
The foregoing factors make up the traditional collection of data relevant to generic credit scoring. However, to effectively rate the credit risk associated with a commercial UAS operator, a lessor needs to also consider factors particular to UAS. These are, in part, spelled out in Part 107. Others relate to risk associated with intended use of a specific UAS.
UAS industry factors include whether an operator is Part 107 qualified, how many of its employees are qualified, the total number of commercial UAS flight hours a Part 107 remote pilot possesses, the number of years of experience operating commercial UAS and the number of years an operator has held a Part 107 remote pilot certificate. The existence of any FAA enforcement actions or UAS incidents are also key.
UAS airframe specific risk factors include airframe quality, type of flight software package installed in the UAS, type of payload being carried, such as whether a UAS will use Lidar to measure inventory or a tank system to spray agricultural crops, advance rate, high or low UAS usage, UAS condition, and lease term based on equipment quality. These factors would be included in the application.
The weight given all of the characteristics listed above will depend on a lessor’s appetite for this type of collateral and risk. However, many of the UAS specific factors would be weighted more toward an automatic acceptance or rejection. For example, if an operator is not a Part 107 qualified remote pilot, or has no employees that are, the application would automatically be rejected. So too would one for a UAS employing an inferior flight software package. The former impacts profitability as it is a violation of Part 107 for an unlicensed operator to receive compensation from UAS operations. The latter affects risk – an inferior flight software package increases the likelihood of injury to persons or damage to property in addition to the destruction of the UAS itself.
Build the Model
Having assembled the traditional and UAS specific factors, the correlation between them must be considered, and points or weights mathematically assigned to them. With respect to traditional factors, points or weights from a generic model could apply. The UAS specific factors are layered over these and assigned relatively higher rates to drive the accept or reject decision. As lessors gain more experience, points or weights assigned can be refined.
Once the model is constructed, a cutoff score must be set. Depending on a lessor’s goals, the cutoff score can be set to maintain a given rate of approval while keeping bad rates low, maintain a given bad/charge-off rate while maximizing approval rate, maintain a combination of approval/bad rate acceptable to the lessor, or optimize profit while minimizing risk. Depending on portfolio data available, the last goal maximizes portfolio performance. If data is scarce, any of the first three are a good starting point.
Implementing and Validating the Model
Implementation includes training employees how to use the model, determining tolerance for overrides of auto decisioning for approval or decline, and applying judgmental experience to approve or decline those applications that are neither clear approvals or rejections, or that are clustered in the middle ground around the cutoff score. For these, the availability of credit enhancements such as guarantors, lower advance rates or shorter lease terms may sway the final decision.
The task of validation involves continually monitoring portfolio performance to assess how well the model is performing. This is an ongoing task necessary to avoid decline in the model’s performance over time.
They’re Not Coming, They Are Here
Commercial UAS are now operating in the national airspace system on a large scale. This will only increase as the FAA continues to clarify and evolve Part 107. With developments such as beyond line of sight operation of commercial UAS, large operators such as Amazon, UPS and FedEx will begin to utilize commercial UAS in their daily operations. This will inevitably lead to excellent opportunities for savvy lessors to grow their portfolios with commercial UAS leases.