Three Ways Data Can Assure the Transition from Broker to Lessor

(This article by David Gnade and Scott Nelson was originally published in the AACFB Commercial Break newsletter (2022 - Volume 30 - Issue 4) on 12/20/22)

Old business plans are usually much easier described than completed – identify customers who have a need, create a product that meets that need, sell your product, make a profit. One of the oldest and most common such business plans in equipment finance is the broker-to-lessor transformation and goes like this: establish one’s brand as a finance broker, build customer and lender relationships, grow the business to the point where one can secure funding for the paper and become a lessor. One could say it’s a straightforward three-step process.

Figure 1: The fundamentals of transforming from broker to lessor have not changed – building relationships in both directions from the middle – customers, venders, lenders.

Many successful finance companies have traveled this path. Industry veterans told us that anywhere from 30%-50% of all finance brokers today would like to do the same. Of course, for every successful journey on this path, there are probably ten that either didn’t make it or failed all together because of the investment and distraction to core business operations. But if we have learned anything over the past two decades it is that traditional business plans are either disrupted or accelerated by technology – sometimes both simultaneously. The broker-to-lessor transition is no different.

The fundamentals of the broker-to-lessor business model are sound – they withstand the test of time – but the execution of the steps can be both accelerated and the probability of success improved with the use of data.

Establish the brand

Establishing a brand in equipment finance is, at least, a two-sided battle. As Figure 2 shows, the first side is establishing relationships with customers looking for access to equipment. The broker must find these parties and convince them that he/she can solve their equipment financing problem.

Figure 2: A customer’s view of the leasing process highlights the roles of the broker (Origination) and lessor (Underwriting, Funding, and Servicing).

This leads to the second side of the battle, underwriting and the lenders. Herein lay the opportunity because equipment finance is a sales driving industry that depends heavily on brokers to meet the recurring demand for as much as 1/3 of the book of business as previous deals roll off the books. The customer, of course, wants a single point solution which creates the opportunity for the broker as they own that relationship. This is the point at which the value of data becomes apparent – customer data.

Figure 3: A data-centric approach helps the organization organize its relationships and lays the foundation for using data to improve both speed and matching success between customers and lenders.

Customer data captured in a Customer Resource Management (CRM) system immediately do three things for a broker. First, it facilitates the customer relationship via communications, marketing, and convenience in application processes. Second, it establishes the basis for business intelligence framework that helps analyze customer characteristics, needs, and matching (approvals) with the broker’s lending partners.

At this point in the journey success is all about more successful deals. More deals require more customers, better customers from the point of view of lending partners or, preferably, both. A broker who does not capture data about their customers and the outcomes of applications does not have the ability to learn and adapt to the many changing attitudes of the lending ecosystem. A simple CRM deployment connected with a business intelligence framework built for the equipment finance workflow empowers both strength and growth of the broker’s business.

Understand your niche

Equipment finance is an industry of niches: yellow iron, trucking, construction, mining, lumber, medical equipment, office equipment, IT equipment, mid-ticket, small ticket, large ticket, to name a few. A key for any business in an industry of niches is finding one that works and exploiting it. Data analysis and associated business intelligence provide insights into niches as a broker engages more customers, more vendors, and more lenders. And one of the best things about origination data in a digital broker system is that data and outcomes – approvals, amounts funded, rates, terms, etc. – are generated rapidly.

Figure 4: Past application and approval data enable the implementation of AI predictors to further accelerate the business through better or fully automated customer-lender matching.

Rapid data generation is valuable because when that data is captured in a appropriate data structure is then available to machine learning and AI. Figure 4 shows one way a broker can accelerate and improve productivity using data and AI. Historical approval data from previous applications with lenders enables the broker to predict both approval and the best lender for a given deal. Brokers encountering manual portals with multiple lenders can significantly improve operations by identifying successful applications and submitting them to lenders who are more likely to match. Better deals faster with data and AI.

Secure funding and build the portfolio

At this point in the journey the business is operating faster and more effectively for both customers and lenders. If the broker is servicing vender partners those partners will also experience more and faster success getting their equipment out the door with the broker’s help. A broker operating at this level of sophistication will be looking at taking the next step – self funding or leveraging a funding-line partnership.

This is where the digital infrastructure described above and the data it has documenting the business’ success will disrupt the normal transitionary period in this stage. The business will have to add three functions – underwriting, servicing, and funding to the digital system as shown in Figure 5. Underwriting will be an automation step using a Lease Origination System (LOS) or modification of the CRM workflow because by this point the broker will know the credit characteristics of successful deals for their customer base. The business can buy or outsource Servicing of the portfolio which will add a data stream from the Contract Management System (CMS) that includes additional data on delinquencies, residuals, and a range of contract performance data. When this data is added to the existing streams of front end data the company will now see the financial performance of the portfolio as well as programs that the firm established with venders.

Figure 5: An established “front end” digital framework becomes the foundation for the lending and servicing functions while providing validation of brand success and risk management for outside funding partners.

The last step in the transition from broker to lessor is often the most difficult because it involves capital which means trust – the broker leadership must gain the trust of a funding partner that they can not only find good business but service it profitably. This is where the data gathered during the growth stage will be critical. The broker team will be able to show the characteristics of deals it will be funding – equipment class, terms, FICO & PayNet scores, as well as typical contract rates indicating credit risk. This history of how the business has succeeded along with a commitment to continuing with those business policies in the associated niche will enable the funding partner to match the prospective portfolio to their credit policy – with data.

Conclusion

A successful transition from broker to lessor provides increased profit margins, better cash flow and the ability to survive downturns in the economy. But the move from broker to lessor is major business change that has always allowed little to no room for missteps. But today finance brokers have the opportunity to capture and leverage their customer and vendor data to make the transition to funding and servicing their own deals faster and easier. Business intelligence applications not only help run the broker business model better and faster, but the historical record also provides vendors and lenders the proof of work that they need to help with the transition. The credit and portfolio data captured and curated with AI along the way will allow brokers to approach banks and funding sources to raise capital with data not available to brokers who do not engage the digital workflows.

The steps from broker to lessor are well known and the path well worn. But the devil is always in the details and the details are much easier to see when the broker leverages their data. So put down the spreadsheets and find those partners who can help you use your data to make the change.

Written by

David Gnade

Senior partner of Fairway Capital, LLC and member of the Tamarack Technology Advisory Board

David Gnade is a 30-year veteran of equipment finance as an executive sales leader with a specialization in vendor-financing. David is a senior partner of Fairway Capital, LLC which provides customized equipment financing to the golf industry. David is also a member of the Tamarack Technology Advisory Board.

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and

Scott Nelson

President & Chief Digital Officer, Board Member

Scott Nelson is the President & Chief Digital Officer of Tamarack Technology. He is an expert in technology strategy and development including AI and automation as well as an industry expert in equipment finance, Scott leads the company’s efforts to expand its impact on the industry through innovation using new technologies and digital transformation strategies. In his dual role at Tamarack, Nelson is responsible for the company’s vision and strategic planning as well as business operations across professional services and Tamarack’s suite of AI products.

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