Equipment finance is an economic engine built on the use of time in operations, specifically the time value of money. So, it is ironic that most enterprise tools and workflows that companies use daily operate on monthly data. They are not real-time systems.
Lessors and lenders almost exclusively use historical data to make underwriting decisions and then often service the contracts for three to five years to see if the results meet expectations. The most important trait of any lessee is their propensity to pay – past tense. Equipment residual value, one of the most important parameters of any lease, is predicted at the point of origination based on historical “comp” data and then assessed at term when “the damage is done.” Lessees often enter contracts believing the equipment will be “good enough” for the life of the lease only to find that they need upgrades to remain competitive. Traditionally, equipment finance payments are designed to be level over time. Lately, we are seeing customer-centric lessors offering flexible solutions because, for the lessee, things change when the rubber meets the road.
Digital transformation requires business leaders to embrace the use of data in everything they do, the omnipresence of cloud technology and the agility facilitated by data analysis. Engagement of these principles, like any organizational change, requires strong leadership to create a culture that views the world in a data-centric way and will change operational behaviors accordingly. One value must run through these principles: real-time data is the key to future success.
Historically, we experienced the concept of real-time data in analog systems like driving a car and heating or cooling a house. But digital has changed the value of real-time data because it makes that data portable, accessible and more usable. Equipment finance in the days of analog – paper contracts and accounting spreadsheets – had little use for real-time data. But in a digital world, the value of real-time data is increasing and we must learn to take full advantage.
Automation of Workflow
The first place we saw real-time data impacting equipment finance was workflow – more specifically, origination workflow. The origination process needs information from many places – credit, titles of ownership, funding, company financial status – and wants this information in real-time to meet customer expectations of time and convenience. Over a decade ago, we created a product called BridgeWare to provide real-time access with Contract Management Systems (CMS). This software solution gave our customers and us early experience with the power of real-time data in workflow automation.
Modern systems are leveraging synchronized real-time access technologies such as APIs, ETL (Extract Transform Load) and data streaming (e.g., Apache Kafka) applications. These technologies open the door to automating any part of the lease life cycle, not just origination. Collections, tax compliance and other portfolio servicing functions can be accelerated or even fully automated with the combination of business rules and access to real-time data from the equipment.
Automation of Risk Management
Early analog control systems often created alerts, simple things like level detecting. “The tank is almost full – turn off the flow.” Digital removes all boundaries from detection because now we can derive measures from data and even combine contexts to create alerts not available within the system’s proximity, e.g., ownership change, location issues, changes in credit status, changes in payment on other commitments and residual values. Real-time data enables the automation of risk identification and thus risk mitigation or management. Lessors no longer have to wait for the risk-state of a low residual value at lease term; they can identify it as it happens and gain valuable time in its mitigation.
A good example of this is real-time location data from transportation equipment. If the equipment is not moving, it is likely not generating revenue thus putting payment at risk. If it has moved outside a normal range or defined-use location, it may present a recovery risk. Real-time access to the location data can automate a range of alerts to facilitate proactive risk management.
Prediction
Without a doubt, the most powerful value of real-time data is prediction. Again, early real-time data controls, whether analog or digital, relied upon simple models. Drawing a card from a deck has a simple prediction model – one chance in 52 of picking the ace of spades, one chance in four of picking a heart. Static models are basically probability functions. Credit models, for example, provide a probability that a borrower will pay. Probability is a form of prediction, but one that is static and does not converge over time.
But real-time data enables equipment manufacturers to create digital twins of their equipment so operators can deploy those models in a digital system that can learn from its past. Over time, the equipment model predicts when it needs help. The models that use real-time data in so-called prediction machines can adapt to the point of proactive action. Real-time models can affect a kind of time machine. They allow action as if you already knew what was going to happen – as if you had already been there.
Equipment finance is an industry ideally positioned to capture the increasing value of real-time data in digital systems. The workflows have moved to mobile and connected systems that facilitate automation. The desired outcomes are well-known and relatively few, making both monitoring and predicting easier. Lease accounting rules define simple machines for which to calculate both progress and risk. Unlike the stock market, equipment finance is more predictable because it is not subject to social behaviors.
Digital transformation is part of everyone’s strategy today, but efforts will fall short if the organization does not embrace the increasing value of real-time data. Real-time data can predict the future, and those looking to the future are the ones who will build it.