On February 15, I shared insights with members of the American Association of Commercial Finance Brokers (AACFB) on how to “Get ahead with data.” AACFB’s Monica Harper hosted Allen Jones of Copernicus and me for a discussion regarding data-centric technologies that can help brokers improve operations, grow faster, and transform into companies with broader offerings.
For those of you who want the highlights, here are my takeaways from the webinar.
The discussion started with Allen laying the foundation for the discussion with an introduction to the systems approach to using data and then share 10 Requirements for Software Systems in the equipment finance space. He identified important characteristics of picking the right solutions and partners including Ease-of-use, Easy Integration, Flexibility, Support, and Affordability. His introduction introduced decision parameters for choosing and integrating new software in a finance broker business.
From this foundation, I layered on Tamarack’s point of view based on our experience with equipment finance software systems overall and business intelligence (BI) and AI products specifically. We walked through the typical design thinking process of “Why, What, and How?”
The Why is critical because it defines the reason for investing in new software as Allen described. The bigger the Why, the bigger the investment that can be supported.
The “What” is the basic software systems used in a standard origination workflow and showed how BI and AI products “amp up” the performance of the organization.
The aggregation and subsequent analysis of data from the disparate systems brings an overall view of the business that informs and thereby accelerates decisions. Any decision is easier and faster when the context and details of the risk are available in a quantified form. We see this in our customers as soon as we turn on the DataConsole and they begin using the management reporting tools.
The next level of speed and accuracy of the decisions then comes with machine learning and AI applied to the combination of historical and real time operational data. The historical data enables the company to build models for outcomes that then become predictors of those outcomes. The predictors further accelerate human decision making as well as provide the tools for full automation of decisions when the risk is understood and manageable. A fundamental of AI-based systems is that the AI is allowed to learn from mistakes, so the mistakes have to be bound to be safe to the organization.
We then parsed the standard lease origination workflow to focus on the experience of brokers with both the process and the software tools commonly used by lessors. Most brokers are aware of or use Customer Resource Management (CRM) software to begin the journey of capturing customer data. Then we noted how portals can define the interface between brokers and lending partners. Those portals are often the last touch brokers have with the workflow so the remainder is often hidden by a distant fog.
But modern Lease Origination Software (LOS) can remove that fog and give brokers the opportunity to capture more process data as well as implement Robotic Process Automation (RPA) by integrating one LOS to the next. At this point brokers can add business intelligence and, in turn, capture enough outcome data to implement AI for outcomes like deal tier and lending partner. When brokers engage the LOS software also creates the opportunity to add BI and AI software to further accelerate and improve their business. We describe a 5-step data-driven process to complete the transformation from broker to lessor by creating the “proof-of-success” that funding partners need to provide the funds to begin holding and servicing deals
Our presentation ended by addressing Allen’s point about affordable. Modern software partners use a Software-as-a-Service (SaaS) service model that enables each organization to switch from the challenging evaluation of a CapEx investment model to one of here-and-now OpEx. Brokers are used to thinking about both revenue and profit in the form of commissions and the right SaaS partnership will enable a broker firm to look at the software as a type of commission on the deal flow – a pure OpEx model where in the providing partner will be motivated to help grow and transform the broker business as this will grow the partner’s business.
During the Q&A portion of the webinar, three questions stood out:
1. What are the most important things that a funder will request on step #4 of the Broker to Lessor transformation slide?
The quick answer here is establishing trust with a good description of the kind of business the broker provides, the productivity/pace of that business, and the successes the business has seen with lending partners.
2. What kinds of surprises do your customers have when they start capturing and looking at their data?
We have seen our customers surprised by the higher-level view that good BI data provides. They are often surprised by concentrations of either assets or industries. They are sometimes surprised by stand out performance by individual reps or vendor partners. I think they are surprised by the curiosity that access to data creates followed by answers to questions previously considered but never asked.
3. How much should brokers anticipate spending to get started in this transformation?
This one is a classic “It depends” question because there is a suite of software solutions that we discussed. But both Allen and I commented that a brokerage can get started for $3000-$5000 month depending up on the scope of the software and size of the business. Remember that a good SaaS partner will cost more if when the brokerage gets bigger – an OpEx model that protects margins.
More on the topic
This is a topic I’ve written about several times. Check out these recent blog posts for more discussion.
3 ways finance brokers can empower themselves with data
3 ways data can assure the transition from broker to lessor
Dark data empowers brokers to be better faster matchmakers
How do you eat a digital elephant? One byte at a time.