Five steps to organizational transformation in equipment finance using AI
Change in business is hard. Creating real and lasting change in business is even harder. But every business knows that change is also inevitable. The current dynamics will change — it’s going to happen. So, during such times, like now, when market forces converge to make change necessary and urgent, leaders need to be prepared and ready to adapt to new economic conditions.
Over the past decade, modular cloud architectures and the associated ubiquitous connectivity that they demand have created a digital fabric that is now fully operational at the socioeconomic level. People – customers, employees, regulators, investors — all operate in a connected mesh of networks and applications that are available anytime, anywhere via the cloud.
The 2023 collapse of Silicon Valley Bank, wherein a majority of depositors moved or attempted to move over $42 billion in less than one day, showed us the new reality in customer management in our connected ecosystem. Never again will a business consider a customer negotiation as a one-on-one, what-you-see-is-what-you-get relationship event. The need to deal with customer urgency in real time is now mandatory.
A foundation for sustaining competitive advantage
Equipment finance companies today are flush with opportunities presented by data. Digital origination, contract servicing, and accounting systems have gathered data on business-critical outcomes like underwriting, funding, and payment delinquency for as many as thirty years for some companies. Most of this data is dark – rarely accessed or used by management in daily operations – even though it holds the historical record of the competitive success of the business strategy. The modern tools of machine learning and AI are ready to exploit dark data to improve competitive advantage and overall business performance.
The next stage in equipment finance automation
Like other service industries, equipment finance started its automation journey in the early 1980s with software. The increase in computational power at ever lower costs enabled companies to automate calculations, communications and eventually transactions with partners and customers. But highly regulated industries like healthcare and finance held on to paper documents and human engagement much longer than other industries - retail, manufacturing and agriculture exponentially increased productivity with automation as a result of digital technology. The good news is that equipment finance has continued its digital journey and gathered tremendous amounts of data along the way. Equipment finance companies today still have disparate data streams and as a result, struggle with both comprehensive business intelligence and how to use that data. But the combination of an enterprise’s dark data with modern, open, cloud- based software platforms including machine learning and AI tools will enable finance companies to enter the next phase of automation: prediction. performance.
Innovation that makes remote work work
Many lessors prospered as the uncertainty of the pandemic moved customers away from capital purchases to the shorter-term and cash flow-based commitments of leasing. But, like much of the business world, many lessors have learned that traditional in-person workflows no longer work in a hybrid home-office work environment. The new norm of remote work demands innovation in the way businesses operate and how teams work together.