Artificial intelligence is far from a new concept in the leasing industry. However, research and industry feedback suggest that businesses are only scratching the surface of what the technology can do to increase efficiency and improve decision-making. Paul Golden writes.
Respondents to the Siemens research paper The Digitalization Productivity Bonus – What value does digitalization offer manufacturers? estimate that potential annual manufacturing productivity gains from digital transformation will total between 6.3% and 9.8% of overall revenue by 2025.
The paper explores the funding options for manufacturers looking to upgrade to the automated operating platforms of Industry 4.0, from the acquisition of a single digitalised piece of equipment right the way through to financing a whole new factory, or even the acquisition of a competitor.
It observes that these finance arrangements tend to be offered by specialist providers that have a deep understanding not only of how the digitised technology works, but also of how that technology can be practically implemented to deliver all the benefits of digitisation.
The paper’s authors suggest that, at times, the financing arrangement will be an embedded component of the value proposition, offered right at the beginning of the sales cycle. In other cases, the technology provider will refer its customer to one or more finance providers to fund a sale.
According to the authors of the paper, complete solutions should be taken into consideration in order to identify the best finance package to effectively digitise a manufacturing facility’s operation, from equipment and software through to the production line and the whole enterprise.
Brian Foster, head of industry finance at Siemens, observes that the take-up of robotics technology with onboard artificial intelligence (AI) is surging ahead, and smart flexible finance is playing a crucial role in enabling organisations to make these investments in a commercially sustainable manner.
A 2016 report by the International Federation of Robotics predicted that the deployment of industrial robots would swell to around 2.6 million units in 2019. According to the report, over two-thirds of industrial robots are deployed in the automotive, electronics and machinery industries, with the electronics sector experiencing the strongest growth rate. “We immediately think of robotics in machine assembly, from the micro-electronics industry up to building machines, cars and aerospace engines and controls,” explains Foster.
“In fact, there are plenty of applications of AI and robotics in less high-tech outputs. A good example is printing and packaging, where recent years have seen a number of innovations when finishing documents – where job set-up and complex multiple angle operations all happen automatically within the single machine unit, reducing delay between jobs.”
Another key sector is the machine tools industry, which has to operate within very tight tolerances to manufacture components to micro-precise standards. Here, machine learning is being deployed to make tiny adjustments and improvements that have a disproportionately large effect on product performance and manufacturing efficiency and quality.
Of particular interest to financiers is the way AI and machine learning can affect two factors – uptime and productivity. Uptime is being improved by predictive analytics which track equipment behaviour and spot impending faults before they occur, which means that maintenance intervention happens pre-emptively and is scheduled for quiet production periods or times of day, so that manufacturing productivity is not interrupted.
“Such analysis is also being used to spot ways of refining manufacturing processes and protocols to further improve throughput or reduce faults,” says Foster. “The interest for financiers is twofold. First, the fact that a manufacturer can improve productivity through technology investment forms a crucial factor in how deals can be structured. Second, the ability to track equipment and technology usage and performance provides a financier with near-real-time insights that provide important ongoing transparency of how the asset is being employed or deployed.”
Richard Ryan, partner at consultancy Invigors, refers to research conducted with Alta Group colleagues in the US on the status of digitisation across a number of leasing companies in North and South America and Europe. “One of the questions asked in the survey related to the importance of various technologies to companies’ digitalisation
initiatives, and it was interesting to note that 70% of respondents considered AI and machine learning to be important or very important technologies,” he says.
“This was one of the higher scores to that question, with AI being rated ahead of telematics or blockchain in importance, although behind mobile apps and predictive analytics.”
However, whether leasing companies are well placed to exploit technologies such as AI is a different matter. Barely one in four (27%) of the survey respondents thought their existing IT systems and infrastructure enabled their organisation to exploit new technologies such as AI effectively – most believed they were constrained, by legacy systems in particular.
The availability of skills was also a constraint, with 71% of respondents seeking to recruit specialists with skills in AI, machine learning or natural language processing, all of which were seen as highly relevant to their digitalisation initiatives. “Most new technology initiatives (such as AI applications) tend to originate in the vehicle leasing and fleet management sectors, as these tend to be somewhat ahead of the curve in terms of innovation compared to equipment finance,” adds Ryan.
“However there are some sectors, such as agricultural and construction equipment, where our research identified captives working on applications involving AI and the Internet of Things to develop pay-per-use leasing solutions,” he continues.
“We also found a European bank-owned lessor planning on implementing machine learning to compute risk adjustments needed for compliance, and an independent smallticket lessor in the US developing an AI strategy to leverage its internal historic data for residuals and risk.”
At last year’s Linedata Exchange Europe conference, Bertrand Cocagne, head of product at Linedata Lending and Leasing, observed that in small-ticket and auto finance, some lenders are using automated credit scoring to underwrite almost all deals.
However, he adds that trust is not there across all segments of asset finance, and that in the more complex and bigger-ticket credit cases, AI is no more than a guide to help humans validate decisions.
Automatic credit scoring and processing is arguably the most advanced use of AI in asset finance. In theory, masses of back-office data built up over decades around the customer, their assets and payment history should inform the decision on whether to accept or refuse credit.
The sticking point is what types of criteria are being used to reach the decision, and whether this is in contravention of fair lending laws and regulations. In the absence of being able to predict how a system is going to use the data, businesses need to be extra vigilant with the type of data being input, and must also be able to explain why the decision was reached, warns Cocagne.
Simon Goldie, head of asset finance at the FLA, says his members are focused on the next generation of technological innovation to see where the leasing opportunities will be. “Some firms are already exploring the use of AI for credit checking and speeding up decision-making, but it is unlikely to fully replace the human element in asset finance transactions, as the service is highly personalised with customers and funders preferring to talk face-to-face about both the asset and the best way to finance it,” he adds.
Among the bank-owned leasing providers, BNP Paribas Leasing Solutions recently launched a European programme to determine the most relevant use cases for its business.
The Stretch Your Business programme has been implemented to predict the potential of disruptive technologies in order to make them part of the company’s business development.
Over the course of 2018, BNP Paribas Leasing Solutions is organising creative sessions to select concrete use cases out of a particular technology, providing an incubator to help the best use cases in each country to mature, so that a first prototype can emerge, and finally bringing together the best three prototypes to develop them into a “minimum viable product” format for testing.
“We do not think that AI has really started to change the way business is done, but it is certain that this will happen rapidly over the next few years,” says François-Régis Martin, chief digital officer BNP Paribas Leasing Solutions. “Many areas – both business and efficiency – will be impacted by the opportunities offered by this disruptive technology.”
While observing that almost all the markets the company operates in are natural candidates for AI, he also accepts that customer comfort with the use of AI and the ability of the industry to accommodate those who are uncomfortable interacting with machines rather than people are key issues.
“This is not just a generational issue – AI is already present and divisive in our personal lives,” he adds. “One of the key aspects will be to support customers and employees to ensure a transition that will improve the entire value chain.”
AI generates vast quantities of data. A key issue for the lease industry is whether data management has become a major challenge, and how GDPR will affect the way customer data is stored and managed.
Data is now at the heart of business strategy and the leasing industry is no exception, says Martin. “These are huge programmes whose GDPR component is only an emerging part.”
The digitisation of society is changing consumer behaviour in various ways. Perhaps most importantly for the leasing industry, it has prompted a shift from ownership to subscription to services, and consumers are getting used to easy, personalised digital access to services.
As a consequence, vehicle leasing is moving towards subscription-based mobility services with digital user engagement tools, observes Sanna Pöyhönen, head of data at LeasePlan Digital. “AI is present in every aspect of this new business model, from personalisation of services to optimisation of customer experience as well as internal business processes,” she says.
“Looking to the future, this could reach as far as connecting mobility services to AI-driven infrastructure for smart cities. This is a key focus as we work to deliver any car, anytime, anywhere.
“We aim to provide digital services, on demand and in a personalised way to make our customer experience as smooth and simple as possible.” Pöyhönen notes that AI is already used by most consumers in the form of intelligent driving instructors, Google Maps and other digital services.
“The transition to AI requires more of a change to consumer behaviour than a technological change and this is the same in all industries,” she suggests. “In the short term, customers will still be able to experience a mix of different channel and contact points.
This must – and will be – a synchronised experience, no matter whether the customer is on a website, speaking to a call centre or in a delivery store.” According to Pöyhönen, user-centric data collection and management is a crucial step in entering fully digitised, intelligent leasing service processes. At the same time, consumers are increasingly aware of their data privacy and leasing companies must prioritise protection of their users’ data.
Companies that treat user data as a strategic asset – such as Facebook and Google – are technically able to do this, but for more traditional industries this presents new challenges.
“LeasePlan has always taken privacy very seriously and our approach to data protection has already developed rapidly over recent years,” concludes Pöyhönen.
“For example, in 2015 we became one of the first companies to introduce binding corporate rules across the whole of our group.”
She notes: “Rather than changing our approach, we see GDPR as an opportunity to enhance our existing data protection procedures while supporting the delivery of future services.”