Growing companies in need of leaders tend to promote their best technical people to fill gaps in a growing organizational structure. The classic problem in doing this is that these technicians often make terrible managers without additional training. Your best developer, analyst or salesperson knows they can do the job better than others, and so they hesitate to delegate and frequently blame their inability to hit targets on the quality of their people.
To succeed at their new level, novice leaders must learn to coach the things that brought them success in the first place. Unfortunately, many new supervisors fail because they cannot adapt to a new set of skills which are outside of their comfort zone. There are a number of great books that deal with this issue, such as What Got You Here Won’t Get You There by Marshall Goldsmith, or The First 90 Days by Michael D. Watkins. Experienced managers are aware of this problem and understand they must challenge and sometimes compel people to grow beyond what they knew yesterday.
Just as individuals have barriers that inhibit personal growth, so do organizations. Perhaps in no industry is this more evident than in auto finance. The industry boasts hundreds of small, independent lenders, many of which have been in business for 30-plus years. Others have grown large rapidly yet struggle with credit quality and profitability. Very few lenders are old, large and stable as the road to becoming so requires a literal evolution in thinking about how the business must operate.
The auto finance industry is one with a tremendous amount of creative destruction. Existing lenders are routinely acquired or liquidated, while new companies form every year. New lenders come from a variety of sources. Some grow out of auto dealerships, others are started by industry veterans and still others are formed by investors from outside the industry. Each of these profiles carry with them the potential for blind spots as the company grows, limiting their ability to move to the next stage of development.
Growing auto finance companies go through a number of distinct phases from a startup to a multibillion-dollar portfolio. These phases are marked by levels of sophistication in information technology, risk management, automation, modeling, reporting and the presence of a data warehouse (refer to the table below). These phases are marked by the following attributes:
Stone Age – Lenders at this stage have portfolios that are usually under $50 million. They do not have a data warehouse but use their software systems as the master data file. There tend to be no risk managers or professional technology staff. No automation is present and credit programs are all judgmental. Without the ability to link origination data to actual performance, these companies struggle to compete against larger entities, and often opt to become a provider of the unique approval with dealers. Note, the moniker “Stone Age” is not meant to be insulting, but simply to describe a level of technological sophistication. It is better to be a profitable Stone Age company than to have an unprofitable billion-dollar portfolio for certain.
Bronze Age – At this stage, typically $50-$100mm in portfolio size, leaders realize the need to get a handle on their data. These organizations typically hire a few “help-desk” level technology people and begin to extract data from source systems into spreadsheet reports to form the basis of business intelligence. With the increase in size, usually preceded by better funding facilities, senior managers begin to focus more heavily on the collections staff and processes surrounding this function. One of the dangers at this stage is when management tries to scale the collections or buying process to merely be a larger form of what they did with a $10mm portfolio. This is the time when management should consider more expensive, seasoned operations managers (i.e., ones who have managed larger portfolios). They will more than pay for themselves in what they can do to move the company forward.
Iron Age – When lenders get to this stage, the CEO and CFO are acutely aware of how much inefficiencies are costing them. By this point, the company will have a true I.T. department managing a sophisticated data warehouse. Reporting is automated, and the beginnings of a risk management department start to take shape. Risk management tends to be focused on policies and exceptions and is often made of operations people as opposed to professionally trained risk managers. The danger for lenders at this stage is that many managers who were with the company from the beginning have outgrown their ability to transition to running a more sophisticated operation. Often, these managers known they are over their head and focus on entrenching themselves rather than broadening their skill sets.
Industrial Age – Lenders at this stage tend to have portfolios greater than $200mm, but not always. I have seen many smaller lenders develop rapidly to a level of sophistication where their I.T. departments and risk teams are quite advanced. Risk management teams tend to be staffed by a dedicated group of experienced professionals, and the technology group has functions that extend beyond hardware to data security, data governance, disaster recovery and other key areas. At this stage, lenders tend to incorporate models to optimize credit underwriting and collections.
Information Age – In the most advanced state, lenders have dedicated decision science or statistics team members building models for all aspects of the business internally. Buying is centralized and partially or fully driven by automated decisioning. The organization is continually learning from what is working and not working, as they have a highly sophisticated grasp of their own internal data. The danger for companies in the Industrial and Information Ages, however, is that they become somewhat inbred on knowledge. There is a belief that anything good will be developed internally. Lenders should be careful to make certain they are aware of what is going at other companies and research firms and should build a culture that embraces challenging internal assumptions.
Barriers to Growth
Every company that achieved scale can attest to a number of growing pains along their journey. Some involve the ability of early stage managers to handle more responsibility, while others include the challenges of continuously raising debt and equity capital to fund more originations. While there are many potential barriers, two primary ones relate to operations. Those barriers are an unscalable buying operation and inefficiency in collections.
Unscalable Buying Operations
Lender’s usually begin with one person who fills the role of the credit expert. They most often have common sense judgment regarding down payment, recent credit issues, first time buyers and which vehicles they should avoid. Most often, this person has a significant amount of industry experience, but not always. Some are smart people from outside the industry (i.e., a dealership or another area of lending). Over time, these experts will usually iterate to buying practices that make them money in terms of balancing pricing versus credit loss.
While a lender needs this in the early stages, the credit expert(s) can become hugely limiting for the company’s growth and development. First, the expert’s intuition is difficult to impart on new buyers. Each new underwriter has a slightly different take on “good judgment”, causing the expert to get mired in labor intensive oversight. Those that do try to formalize every contingency develop programs that are so complex that execution suffers, and exceptions are rampant – making it nearly impossible to scale the operation.
The second issue is that without a data warehouse and crew of qualified analysts, experts often make false correlations between their judgment and credit outcomes. Consider a deep sub-prime portfolio with a 50 percent default rate, and a 35 percent vehicle recovery rate. Lenders in this space typically charge a 24 percent APR with a 30 percent discount, which equates to nearly a 45 percent gross yield on paper with a 20 percent annualized net loss rate.¹ That kind of spread between yield and credit loss covers many sins, and often leads to a false sense of security that financial results were uniquely the result of underwriting rules.
Don’t misunderstand me. There are many small lenders that do a very good job selecting customers, evaluating collateral and structuring deals. The issue is advancing to the next stage of growth. Internal experts tend to reject widely vetted tools such as credit scoring models when the score disagrees with their intuition. They also believe that larger lenders, often with sophisticated models, have insane buying practices because they are pricing deals too low or go after deals the expert would have rejected.
What the experts don’t understand, or refuse to accept, is that lenders who have embraced sophisticated credit tools bring consistency to their buying practices and a program that is scalable. Most sub-prime lenders approve between 30 to 40 percent of applications. If nothing else, a comprehensive credit scoring model (i.e., one that includes application and loan structure factors in addition to the credit bureau data) could be used to automatically decline half of those applications, allowing the lender to underwrite twice as many deals without adding headcount.
No expert approves a deal they believe will default, yet each of their portfolios have losses. Effective credit scoring models help lenders avoid defaults they would previously have pursued, upcharge on riskier deals, and price more aggressively on higher quality deals which drives up yield and closure rate. Additionally, lenders that operate with objective credit metrics (i.e., scores) have much greater success obtaining larger, less costly debt facilities which allow them to scale their portfolios.
Senior collections managers and servicing executives are perpetually blamed for company performance issues, yet most problems originate elsewhere. This happens for two reasons; first, it takes at least 12 months from origination for serious performance issues to become visible, which means there is not an obvious and immediate link to underwriting quality when credit issues arise. Second, lack of a risk management group or poor forecasting leads to overly optimistic targets for collections managers to hit.
I recently met with a senior collections executive who told me his risk team provided a monthly net charge-off forecast of .3 percent, yet the company has consistently been charging off 8 percent on an annual basis. It doesn’t take a math PhD to figure out that 8 percent divided by 12 months is more than twice that number. When collections managers are held to inappropriate benchmarks, they tend to resist, and even resent, the involvement of analytics people in their department. Furthermore, they tend to throw headcount at delinquency in order to hit an unrealistic number – thus driving up operating expense.
Certainly, I am not saying poor collections do not impact performance; but, in my experience, 70 percent of credit issues are related to what was done when the loan was underwritten. Perhaps 20 percent can be attributed to collections, whereas 10 percent relate to external factors such as the economy. It is critical to evaluate the collections effort based on reasonable expectations tied to objective credit quality measures. When servicing executives see that they are being measured against reasonable goals, they will have the confidence they need to embrace quantitative tools that may be used to optimize results.
When a lender is small, with less than 10 collectors for example, servicing optimization doesn’t really matter. However, when a lender begins to grow their portfolio north of the $50 million-mark, efficiency matters a great deal. Typically, 60 percent or more of an auto finance company’s operating expense is tied up in the servicing operation. Adding collectors in a linear manner as the portfolio grows can become a serious impediment to profitability. For this reason, many large and well-establish lenders embrace optimization tools which can improve collections and recovery results while reducing expense by as much as 40 percent.
Breaking Through Barriers
While much of this information may seem intuitive as stated within this article, managers frequently find it very difficult to execute. Senior managers who have had a significant amount of success in early stages are often very resistant to changing their mindset. There is a quote that is often attributed to Confucius that goes, “If you are the smartest person in the room, then you are in the wrong room.” Whether he said that or not, the sentiment is absolutely correct.
Many executives, particularly at smaller organizations falsely believe their situation is completely unique within the industry, and so they dismiss what they could learn from others. For that reason, they will continue to be anchored to the status quo. Breaking through barriers requires that egos are literally checked at the door, and that the company is focused on becoming a true learning organization. This involves proactively meeting with industry peers that are further down the road in development, and even engaging with technical consultants that have the abilities not native to the organization. Furthermore, lenders must initiate periodic external reviews of the company’s bench strength in each major department to make certain they have what is required internally to move forward.
In October of 2019, I gave a presentation on this topic to the California Financial Services Association. In that speech, I covered a number of other barriers lenders experience in more detail. A recording of the slideshow and audio is available for those who would like to learn more about this topic. Click the video below to view the presentation.
¹ 50% default x 90% principal outstanding at default is a gross loss of 45%. With a 35% recovery, the net loss is 29.25% net loss. Assume an average life of 1.5 years, which produces a 19.5% annualized net loss rate. A 30% discount annualized over 1.5 years is 20%, added to a 24% APR produces a 44% gross yield.