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Drones–officially named Unmanned Aircraft Systems–are experiencing robust growth. During 2020, the drone fleet grew on average by 7,850 units each month. The Federal Aviation Agency forecasts that there will be 1,144,000 drones in the U.S. by 2025.

But the cost of drones is also increasing fast. The acquisition cost of a drone in 2016 averaged less than $3,000 for a reasonably capable unit; today a single multirotor drone and sophisticated radar payload package can approach $75,000 to $100,000.

Drone growth presents opportunities and challenges for equipment lessors to grow market share in this cutting edge, highly technical industry at its inception. 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 drone business in an industry that has only been in existence for a little over 5 years? Credit scoring provides a useful means of assessing profitability and risks associated with equipment leasing for commercial drones.

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. Given the likely size of the drone lease transaction, credit scoring is particularly appropriate. Credit scoring models are used almost four times more frequently to aid in the evaluation of transactions up to $250,000 than for larger transactions. Nearly 90% of equipment lessors use some type of auto decisioning either for approval or decline. 

Benefits and Limitations of Scoring 

Credit scoring has a number of benefits including assisting a lessor with regulatory compliance because if consistently applied, it does not consider prohibited characteristics such as race or age. In the commercial drone 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.

Drawbacks of scoring include the cost and time associated with the development of the model. A scoring system can’t identify with certainty individual good or bad accounts, only the odds that an account will be good or bad. Scoring systems also degrade with time as the population and economy change. They must be continuously monitored and validated to determine how well they perform. 

An effective scoring model for commercial drone leasing will include the traditional factors: 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. This would all come from an application and data obtained from credit bureaus.

Commercial Drone Specific Factors 

To effectively rate the credit risk associated with a commercial drone operator, a lessor needs to also consider factors particular to drones. 

They include whether an operator is licensed by the FAA to operate drones commercial, how many of its employees are qualified, the total number of commercial drone 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 drone incidents is also key.

Drone fuelage specific risk factors include fuelage quality, type of flight software package installed in the drone, type of payload being carried, such as whether a drone will use radar to measure inventory or a tank system to spray agricultural crops, advance rate, high or low drone usage, drone condition, and lease term based on equipment quality. 

Having assembled the traditional and drone-specific factors, the correlation between them must be considered, and points or weights mathematically assigned to them. 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.

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.

The task of validation involves continually monitoring portfolio performance to assess how well the model is performing so as to avoid a decline in the model’s performance over time.

They’re Not Coming, They Are Here 

Commercial drones 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 drones, large operators such as Amazon, UPS and FedEx will begin to utilize commercial drones in their daily operations. This will inevitably lead to excellent opportunities for savvy lessors to grow their portfolios with commercial drones leases.

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