Auto financing is different from other asset-backed loans. Borrowers are:
- More likely to have lower credit scores, and
- Interact with the lender less frequently.
That’s a recipe for big challenges.
The solution? Getting deep insight from every borrower conversation. Here’s how:
Understand every borrower conversation
There are typically a few strategies for understanding what happens in borrower conversations:
- Have QA team members walk a call center floor and note any concerns or flags
- Record a sample 2-3% of all conversations and manually review them
- Review calls with a traditional speech analytics tool (these have about 40% accuracy including false negatives)
None of these options gives you insight that could actually improve your understanding of borrower conversations or helps you improve efficiency.
So the first step to getting the most out of every interaction is to ensure you can automatically review every interaction — accurately.
Here’s the better option:
- Moving from outdated speech analytics tools to a modern AI-powered conversation analytics solution gets you closer to the next improvement: prioritization.
Understand your accounts
Let’s consider what happens with delinquent accounts.
When borrowers miss a due date, your model might carve up the associated risk and relative dollar amount in order to prioritize which borrowers to send communications to and when.
But if you’re capturing the conversations and their context, you have a much better opportunity to prioritize your accounts and keep them out of delinquency.
Let’s say you call Borrower A and Borrower B about late payments. Both borrowers have the same credit profile and balance.
Borrower A says, “I literally do not care,” and Borrower B says, “I lost my job but I really do want to pay you — I just can’t tell you when.”
With old-style evaluation methods, these borrowers would be treated similarly. But there’s useful information in these conversations about sentiment and likelihood to pay that can affect prioritization and other decisions.
Without capturing that information, you’re missing the details about your account that can inform smart prioritization.
And that leads us to the last way to improve auto loan servicing: agency relationships.
Communicate with your agencies
If agencies and auto finance teams have the ability to share all these insights and details about conversation, everyone benefits.
Agencies could build smarter models, sooner, and use the insight on account prioritization to ensure effectiveness.
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 fully recovered in two weeks.
Each borrower would get customized payment options based on their situations.
With access to the conversational data when you pass the account to an agency, you can provide the relevant script for Borrower D. Your agencies won’t have to start from scratch — not only with Borrower D, but with any borrower in a similar financial situation (with a similar sentiment) in the future.
To avoid repossession and collect on delinquencies, auto finance teams need the best agencies, and the best agencies provide clear reports on their success.
The right analytics and insight solution will allow agencies and lenders to easily receive share information and use it to sort through portfolios and trends to make smarter decisions about where debt goes.
Improving auto loan servicing for everyone
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 traditional speech analytics.
A complete solution, with AI-powered understanding of conversations and detailed analytics and insights can help you make more informed decisions about debt and borrowers.
