How to Improve Auto Loan Servicing

Several dark-colored cars parked in a parking area. The perspective appears to be through glass, looking into the enclosed area.
Photo by Erik Mclean

When it comes to consumer finance operations, auto lending is a different beast. Not only is there a physical asset to be managed, but the terms of the loan are typically much longer and more stable than other loan terms, having been established at the point of purchase and lasting the entire length of the loan. 

That stability has an unintended consequence for auto lenders: fewer opportunities to check in with their borrowers to understand the likelihood of their complete repayment. In other words, though you’re already tracking the important metrics and working toward accurate forecasts, your ability to achieve accurate delinquency and loss forecasts is hampered simply because of the lack of transactional or conversational interactions with your borrowers. 

And that’s not the only key difference between other secured loan types, like real estate lending,  and an auto loan. Relative to other asset-backed loans, the auto lending market tends to be willing to capture a higher share of borrowers with lower credit scores, presenting a clear challenge: How do you balance efficient loan servicing and the need to actively manage a higher-risk portfolio?

So, auto loan servicers must support borrowers who are:

  1. More likely to have lower credit scores and 
  2. Interact with the lender less frequently. 

That’s a recipe for big challenges to emerge.

Unless, of course, you have access to deep insights about your borrowers and the current economic conditions to draw on. What if you could mine every interaction for the important details?

The smartest auto lenders in the country are doing just that. They’re considering how to achieve higher cost efficiency while strengthening borrower relationships, by: 

  • Engaging borrowers throughout the loan lifecycle with intelligent and automated outreach at the right time, in the right channel
  • Preventing delinquency using account and economic data to better anticipate and avoid repossession and losses
  • When it does need to occur, conducting repo efficiently, communicating with vendors, quickly securing the asset, transporting the vehicle, and moving through the auction process 
  • Ensuring strict compliance across all agents and vendors, including vendor licenses, borrower communication, and different regulations across states.

Improving Auto Loan Servicing Begins Here

We make it sound easy, but getting access to deep insight in a practical, applicable way is the primary challenge. Let’s start there. 

1. Understand Your Borrower Conversations

One of the more difficult aspects of the auto lending process is the lack of conversation between your agents (or agencies) and the borrower. There are typically a few strategies for understanding what happens in borrower conversations, and most are based on QA for compliance: 

  1. Have QA team members walk a call center floor and note any concerns or flags
  2. Sample 2-3% of all total conversations as recorded phone calls and spend time manually reviewing calls as a team
  3. Less commonly, review all calls with an automated speech analytics tool that is, at best, about 40% accurate (inclusive of ‘false negatives’) in capturing phrases and meaning.

As you can see, none of these options get you the insight that could actually improve your understanding of the few borrower conversations that occur — and these strategies certainly aren’t multichannel. (Because auto loan servicing happens across channels and often through vendors, aggregating customer communications is one of an auto lender’s biggest challenges.)

But understanding, deeply, the meaning and context of each interaction among your agencies, borrowers, vendors, and even regulators, is a must if you want to improve efficiency. The first step to getting the most out of every interaction is to ensure you can automatically review every interaction — accurately. 

Given the opportunity to use a better conversation analytics solution, you should take it. That will get you closer to executing the next improvement: prioritization. 

2. Understand Your Accounts

Across your loan servicing process, there are several nodes of analytics activities and operations activity, with the operations activity gathering data for the next analytics process. 

Let’s consider what happens every day with delinquent accounts. When borrowers miss a due date, your model might carve up the associated risk and relative dollar amount only in order to prioritize which borrowers to send communications to and when. But as that model graduates down the line and you work to prioritize your paper (and your time), you might lose context — especially if you assign a delinquent account to an agency due to your prioritization model. 

However! If you capture the aforementioned understanding of conversations, you have a much better opportunity to prioritize your accounts, keep most out of delinquency, and (we’ll get to this in the next section) score and prioritize accounts once they do become delinquent, too. 

How? The answer is quite simple. Better conversation analytics can give you more data attributes to help your prioritization model succeed

As an example, imagine what would happen if you call Borrower A and Borrower B, two borrowers with the same credit profile and balance. On the phone calls, Borrower A says, “I literally do not care,” and Borrower B says, “You’re right. I lost my job but I really do want to pay you — I just can’t tell you when.” 

With your traditional model, these borrowers would be treated similarly. You’d call both back within the allotted time frame. But there’s so much more here. There’s knowledge about sentiment and likelihood to pay. This knowledge can help you adapt your workflows. When you feed them sentiment and additional language cues, your prioritization, agent assignment decisions, and even debt sale strategy models can become much smarter. 

And that leads us to the next way to improve auto loan servicing: agency management. 

3. Manage Your Agencies

Too often, lenders spend a lot of money managing and paying their agencies. But what if you could offer your agencies the analytics that your borrower conversations should carry with them? That’s a game changer: Agencies could build smarter models, sooner, and use the insight on account prioritization to ensure effectiveness. 

For example, let’s say Borrower C and Borrower D both were in car accidents, and both are delinquent. Borrower C hasn’t spoken to you in months and is on long-term disability. Borrower D is going to be better in two weeks. You can offer customized options to each borrower based on their timelines. When you pass the account to an agency, you can provide the relevant script for Borrower D. Your agencies won’t start over from scratch — not only with Borrower D, but with any borrower in a similar financial situation (with a similar sentiment) in the future. 

But individual borrower management isn’t the only way to support agency management using conversation analytics. Even the improvement of your agency’s models isn’t the limit. Indeed, the best auto finance servicing teams know that to avoid repossession and collect on delinquencies, they need the best agencies — the best agencies provide clear reports on their success. The right analytics and insight solution will allow lenders to easily receive those reports and sort through their agency and debt portfolios and trends to make smarter decisions about where debt goes. 

Auto Loans and Prodigal

The bottom line is that interactions are critical to understanding your borrowers and their intent to pay, and to understanding your agencies and their ability to collect effectively. Because of the unique terms of auto financing, auto lending teams are especially hamstrung by typical conversation analytics. Prodigal can get you out of the rut and help you make more informed decisions about debt and borrowers. 

Ready to get started — or just want to learn more? Talk to our team by clicking the button below. 

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Brian Reed

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Karin Krisher


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