Four ways data is changing the future of equipment finance

June 29th, 2022 is the 15th anniversary of an event that I would say was a defining moment for the future of mankind - the introduction of the iPhone. When we consider defining the future, what do we do? We think George and Jane Jetson. We try to divine what people will experience and do in everyday life. The iPhone empowered us with data to change nearly every aspect of our daily and professional activities. In some cases, it is just the ease with which digital systems move the data and information around, e.g., mobile banking, streaming entertainment, Facetiming, and a wide range of ecommerce applications. In other cases, it is the way data has expanded the scope of information available to us as we navigate the decisions we make at home and at work. The availability and analysis of data is the driving force for change in business today and we see five ways that it is going to change the way equipment finance operates over the next few years. We note that “changing the way a business operates” is another way to say “changing our behavior” so we are really talking about five ways data is going to create innovation in our industry.

1. Data science skills replace instinct for decision making

Many equipment finance leaders view their business as relationship-based and often use face-to-face meetings with customers to make the most common risk assessments – credit. The instinct of the business leader or underwriter is often a key part of making a decision particularly when the parameters of the deal are unclear. One equipment finance leader told me that before they started using data they did mostly “story credits” – they created a story of the customer to support the financing decision.

The business of equipment finance is basically risk management. The product life cycle begins with payment delinquency risk and funding partner preferences, then moves to customer financial performance risk combined with macro-economic risks like interest rates ikes and recession. Understanding the consequences of decisions regarding these complex and intertwined risks makes or breaks an equipment finance business. Digital technology has been changing both relationships and the role of instinct in these decisions, but data analytics and data science tools like machine learning (ML) and artificial intelligence (AI) are going to become the most important skills of a finance organization because data based decision making is much more comprehensive in its ability to consider risk parameters, much faster than even human instinct regarding the nature of a business, and much, much more quantitative and precise in the understanding of the risks of various outcomes.

2. Data increases the precision of market segmentation and product customization

A business that embraces risk uses data to understand it quantitatively the way manufacturing operators use IoT data to improve their understanding of the performance of every piece of equipment, labor resource, and supply chain source. Dr. Timothy Chou wrote Precision to explain how more data creates more precision and accuracy for managing risk while operating a business. These organizations leverage previously dark data to better understand how the complex systems of their production operations deliver a variety of outcomes and then implement controls to deliver those with the most desirable impact.

In equipment finance today both products and risk analysis are often done in broad strokes and scoring models rather than assessing the risks of a distribution of outcomes, business circumstances, and market segments. Credit bureaus have begun focusing on market segments, but the economics of selling scoring services limits the specificity of those segments. Most underwriting “scorecards” are simple, weighted-linear models of a few variables – often less than a dozen – rather than the tens to hundreds of parameters used in manufacturing and investment machine learning models.

Figure 1: By aggregating all data in one place, including dark data, an enterprise can better understand and model operational risks to deliver better outcomes.

A company’s data, when aggregated and analyzed, will provide insights by region, by equipment class, by season, by industry, and even by customer to increase the precision of the understanding of risk to provide better products, risk mitigation, and financial outcomes. Data-based models built on past, present, and ongoing data streams can predict the distribution of risk across all outcomes. ML models use many more parameters to segment and focus the analysis to make the risk predictions much more precise than general scores or simple scorecards. The more precise analysis will enable companies to bundle deals by risk level in the same way investment portfolio managers use methods like “the efficient frontier” to deliver a designed return at the lost possible cost, i.e. risk.

Data engineers will have all the data they need to replace instinct with informed decisions.

3. Pace of learning and adaptation sustains competitive advantage

Renowned business strategist Peter Senge once said, “The only sustainable competitive advantage is an organization’s ability to learn faster than the competition.” Data centric organizations will put Senge’s advice into practice in three ways.

1. When an organization focuses on data it becomes curious as to what the data reveals –past, present, and future. Data analysis becomes a learning tool used throughout the culture to find ways to learn from past performance and improve future business outcomes.
2. As the organization begins using data it learns where the hard problems are creating bottlenecks that require human critical thinking and intuition. As the figure shows, a data centric organization will adapt its workflow and use automation to isolate the bottlenecks so that its human capital can focus on important deals to greatest effect and speed.
3. AI models can adapt in real time to assess risk at a situational level to accelerate learning and provide more informed decisions. Prediction enables the business to learn and adapt faster by evaluating defined-risk business experiments and while focusing human resources on the more complex deals with larger business impact.

Figure 2: Data centric organizations use the data generated by the workflow to automate more simple parts of the business and focus human capital to learn faster on the complex.

4. Workflows leverage prediction creating a proactive vs reactive culture

As the company gains confidence in the risk measures of its predictors it will increase its pace of business by deploying prediction automation with appropriately defined business rules, e.g., size of deal, partner, class of equipment, combined with quantified risk policies, e.g., deals in X credit tier with YY% delinquency probability are underwritten automatically. Prediction machines convert “dark data” into accurate outcome models enabling the business to move forward automatically with a quantified risk policy. Further, when undesirable outcomes are predicted, they can be avoided either by removing it from the workflow or by changing the parameters to change the outcome.

Prediction changes the culture of decision making from reactive – “when this happens do this” – to proactive – “If this is likely to happen, then do this now.” Proactive cultures use their data to understand possibilities and create the future outcomes the business desires.

Remote work and selling create a new norm – distributed workflows

COVID accelerated a transformation initiated by the power and convenience of data - remote work and selling. Lockdowns exposed bottle necks in traditional workflows that depended upon proximity or face to face interactions. Even when documents or processes were digital, the management of tasks and queues were often not in place nor clear because “office walkers” had traditionally moved things along from one decision maker to the next. Many processes had single points of failure - if someone fell ill, everything stopped and waited for that person to either re-engage or return to the office. Workflows were designed, and constrained, by documents, the workspace, and convenience of coworker presence. Organizations who were ill-prepared for remote work and the required digital technology found their teams feeling disconnected and the performance of their business uncompetitive – often because it was too slow.

The efficient and rapid exchange of data throughout the workspace will empower the distribution of the workforce. Face-to-face and manual approaches will be replaced with digital solutions throughout the stages of the deal workflow: applications, negotiations, underwriting, documentation, approvals, and funding. The workforce will be fully connected in a mesh-network in which every interaction is digitally captured to document the decisions and grow the knowledge-base of the organization. The workflow is no longer a continuous stream in space or time. It is woven across workforces that may be distributed so widely that all interactions become asynchronous – completed when and where convenient.

Organizational knowledge will become one of the biggest challenges for companies using distributed workflow because, as Simon Sinek points out, employees can no longer ask questions and learn from the rest of the organization during the “in-betweens.” The verbal history of the business, the unstructured knowledge base, will disappear. Information recording, sharing, and access will become the fuel of the distributed workforce.

Conclusion

The past 15 years have seen unprecedented innovation in how we live our lives and do our jobs because data is now central to everything we do. Technology ecosystems like open cloud platforms, the Internet of Things (IoT), and 5G will amplify the quantity and speed with which data is generated, analyzed, and shared. Equipment finance will see data change how we do business. Those who adapt and embrace its opportunity will not only learn faster, they will win bigger.