F&I Products – Virag Consulting

Choose the Right AI Tool

The AI landscape has changed a bit since I wrote What Is Real AI? back in 2021. The advent of GenAI has enabled a new wave of dubious AI sales pitches. Here’s one that crossed my desk recently:

We’ve identified some key GenAI opportunities at PermaPlate … forecast revenue and claims across [products] and adjust staffing monthly.

This sounds like a good idea, except – it’s not a GenAI application. It’s a standard forecasting exercise that everybody does already. If I did want to switch to a learning model – even a deep neural net, which is architecturally similar to an LLM – it would still not be GenAI.

The thing to remember is that GenAI “generates” things, like blog posts and deepfakes. My favorite learning models, going back to AI-Based Risk Rating, are all quantitative in nature. Here again, there are plenty of good statistical methods. Even if you prefer to use a learning model, you may not choose a neural net.

Neural nets have a problem with explainability. They’re basically a black box. That’s why credit bureaus, which must be able to explain their ratings, use a two-step approach. They use AI for exploration and feature engineering, but then they put the features into a more-transparent logistic regression model.

I might consider GenAI in a forecasting application, to deal with unstructured data. On the other hand, I would ask why the data is unstructured. We did this exercise as a POC here at PermaPlate. I wrote a little program that would read a service contract, and then answer natural-language queries.

Which coverage did the customer select and does it include roadside assistance?

It was a cool demo, but – if you want coverage details available for automation, it makes a lot more sense to store them in machine-readable form, at origination time. And what kind of automation might that be? Well, it might be “agentic.”

Agentic AI means that the AI has “agency,” in the sense that it can make decisions and do things in the world. Cool, huh? We give AI agency by equipping it with tools, in the form of software APIs.

Imagine asking ChatGPT to organize your next trip. It can’t, because it’s trapped inside your web browser. But if you invoke ChatGPT as part of an agentic workflow, with interfaces to the airlines and hotels, it can actually book the trip.

Agentic workflows often divide the work among tool-using LLMs, with a mastermind LLM directing the others. For systems that don’t have APIs, the agent can use Robotic Process Automation to operate the system’s user interface – just like you would at the keyboard. It’s not surprising that UIPath, one of the leading RPA vendors, has moved into Agentic AI.

Here is a short list of the latest AI techniques:

Large Language Model (LLM) – Like Grok and ChatGPT, these are AI models that can read and write (and plan, and execute).
Generative AI – Broad class of AI models that can create things, including LLMs but also diffusion models for video and other media.
Deep Neural Net (DNN) – Core technology behind GenAI, and many other learning models, as in my earlier article.
Retrieval Augmented Generation (RAG) – As the name implies, GenAI “augmented” by the ability to find and read your documents. See Unguided RAG for Text Comparison.
Robotic Process Automation (RPA) – Not AI, but frequently used by Agentic AI. As I wrote in Applied AI for Auto Finance, you can derive a lot of efficiency from RPA alone.
Agentic AI – AI agents that can make decisions and act autonomously.

Now that you know the lingo, you can choose the right tool for the job – or your AI sales pitch. I, for one, will not be using GenAI to predict claims volume … but I may use Agentic AI to dispatch the technicians.

Biweekly Payment Magic

A while back, I did some foundational work for a leading biweekly payment service. That is, the math part, which I will reprise here. Biweekly works best in a climate of high interest rates and, unfortunately, soon after this project, the Federal Reserve dropped their reference rate to zero. The Fed has not been persistently above 2% until recently, and biweekly is once again looking good.

The featured chart shows a scenario first constructed by my erstwhile partner Phil Battista. I call it the “magic trick” because the customer in this scenario has financed an extra $3,250 with no change to the term, APR, or payment. Before presenting the trick, here are some basics about biweekly.

Biweekly Payment Plan Basics

In Canada, the banks offer loans with native biweekly payment schedules, and dealers feature them in their advertising. Here in the States, you have to use a service. The service collects payments biweekly via direct debit and manages the lender to accelerate the amortization.

Here is an example. According to recent Cox data, the average price of a new car is now above $49,500 with an APR of 7.0% and a 72-month term. By the way, this survey does not include luxury brands, and some people are financing up to 84 months.

Below, I have modeled this “average” loan showing monthly versus biweekly payment schedules. This is showing the amortization only, omitting whatever fees the biweekly service may charge. You can see that the loan is paid off seven months early.

If you’re using longer terms to fit customers into payments, biweekly will shorten the trade cycle a bit. Also, credit-challenged buyers may be better off with direct debit synched to their paychecks.

Nostalgia Alert: coding for the U.S. Equity project was originally done in C# by my son, Paul, who would have been around fourteen at the time. We were making an OO model to include all loan and lease instruments as subclasses. Coding for this article was done by me, in Python, which is 10X easier.

The Magic Trick

If you compare the two charts above, you can see graphically how Phil’s trick works. Instead of starting your biweekly loan at the same amount and having it end earlier, you start it higher and aim to end on the same date.

The trick works because half the monthly payment is higher than a native biweekly payment would be – by $33 in this example. The customer makes the equivalent of thirteen monthly payments per year, and the bank loses a little bit of interest income. Here are the steps:

Increase the amount financed, which will increase the monthly payment.
Increase the term until the monthly payment comes back down to where it was.
Use the biweekly program to bring the term back down to where it was.

Congratulations, you can now finance more product with the same monthly payment. I covered the concept for menu systems in Six Month Term Bump. To do goal seeking, as I’ve shown here, you will need some Python (or a precocious teenager).

Schrödinger’s Combo Product

NADA has recently published a model policy for properly selling F&I products, i.e., without running afoul of the Attorney General. It includes the disclosure formerly known as the AutoNation Pledge, and a new procedure which seems to be taking the place of the old-school waiver form. I say “seems” because there is no mention of the old form, which I believe has something to do with nuclear physicist Erwin Schrödinger.

Prior to the sale of a VPP, the Dealership will request the customer’s acknowledgement of the election to purchase or decline each selected VPP or VPP bundle.

As everyone knows, subatomic particles exist in an indeterminate state until they are pinned down by measurement. For example, if you have a radioactive isotope of Cesium, you can’t tell whether it has decayed until you aim your Geiger counter at it. Not only can you not tell what state the atom is in, it is not definitely in any state until you measure it.

To show how this contrasts with traditional physics, Schrödinger proposed the following thought experiment. Imagine there is a cat in a box with the Cesium rigged to kill the cat when it decays. According to the Uncertainty Principle, the cat is both alive and dead at the same time.

Similarly, the F&I waiver requires each product to be either accepted or declined. You bought the dent protection, so it prints in the green column, but you turned down roadside assistance. It prints in the red column. To save a few dollars, you are willing to leave your family stranded. Please sign here to confirm.

But what if dent and roadside – and key and windshield – are part of the same bundle? You only bought one of the components, so it would be misleading to print it in the green column. On the other hand, you are not going to confirm declining the bundle, because you did buy part of it. So, in which column does this product belong?

Here are some ideas:

The menu system should account for the child products and print them individually on the waiver. It should also count them separately as product sales.
The menu system should print the coverage description, and the coverage description should state which components were accepted.
Providers should offer bundles all or nothing, and not allow them to be split up.

Unlike Schrödinger, you will not win the Nobel Prize for solving this one – but you can provide some guidance to your fellow F&I practitioners. Click the link below to register your answer.

Workflow for Online Car Buying

A few years ago, I published a precedence diagram for the key operations of online car buying. I was arguing against a linear process, and calling attention to some deadlocks. Since then, I have been following the industry’s experiments with new process models, and coming to realize that these deadlocks are the great, unanalyzed, obstacle to process reform.

Practices that seem unfair, deceptive, or abusive may actually be crude attempts to solve the deadlock problem.

One example of a deadlock is that you can’t quote an accurate payment until you know the buy rate, and for that you need to submit a credit application. This is usually solved by iteration. You do a pre-approval or quote the floor rate, and then change it later.

Likewise, you can’t price protection products until you know the vehicle, but the customer wants to shop by payment. Protection products are also priced by term, and you don’t know the desired term until you finish structuring the deal.

In fact, even the customer’s choice of vehicle depends on the monthly payment, which is downstream of everything else. Virtually the only operation that’s not blocked by another operation is valuing the trade.

Like an interlocking puzzle, “we don’t know anything until we know everything.” Choosing any one item to lock first, without iteration, will result in a suboptimal deal – buying too much car, for too long a term, or overlooking the protection products.

Practices that seem unfair, deceptive, or abusive may actually be crude attempts to solve the deadlock problem. For instance, quoting a payment with some leg in it, or goal-seeking the full approval amount.

Can you see how this ties into current debates about the hybrid sales model? F&I presents a menu with a six-month term bump, which might not be optimal, just to compensate for too tight a payment from the desk.

Fortunately, in the world of online car buying, the customer is free to resolve deadlocks through iteration. This means:

Set up the deal one way
Change any feature, like the term
The change “cascades,” undoing other features
Revisit those other features
Repeat until all features look good together

The in-store process does not support iteration well, and probably never will, but an online process can. All you need is the well-known concept of a “dirty” flag, to keep track of the cascading changes, along with navigation and a completeness gauge to guide the customer through steps #4 and 5.

You could analyze step #3 at the level of a dozen individual features. I made that chart, too, but I believe it’s more useful to collect them into the canonical five pages shown here.

By the way, I have previously described the products page in some detail, along with the analytics to drive it. Discussion of the “random survey question” is here. Today’s diagram contemplates a mobile app, as do my recent posts, but the same approach will work for a web site.