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MODERN CAR FINANCE: OPTIMISING ALTERNATIVE DEAL STRUCTURES
RICHARD COWLEY THE CRIPPLING CHALLENGES COVID AND THE CURRENT COST OF LIVING CRISIS have presented to the motor finance sector will shape the industry’s choices for years to come. But a major problem that was already evident well before the pandemic can be resolved with advanced analytic science. It is an issue facing all those in the industry, not unique to any single organisation. Car dealers want to get more customers through their doors, virtual or physical, and quickly back out again with a signed contract and a set of new keys. Of course, for many, part of the process is setting up the finance and this is where many lenders struggle. Automated systems will reject a finance request without making a compelling counteroffer that could win them the business.
Most auto lenders do not have an automated counteroffer process, which means many lenders handle this process manually, based on simple rules. This
is a time-consuming and inefficient process. The evolution of the online car buying experience is creating a whole new consumer expectation of speed and convenience. Auto finance providers that want to support traditional motor retailers rising to this challenge need to respond with the right tools for the job.
IMPROVING THE COUNTEROFFER Automating the counteroffer process is much more complex than it first appears. It needs to be comprehensive and consistent in line with FCA (or other regional) regulations. Nearly every possible counteroffer needs to be evaluated for acceptance, and then the bundle needs to be rapidly reduced to those that will interest the buyer.
The process should take into account that some buyers care most about the interest rate, some care most about the deposit, while others care most about the monthly payment. It must also be fast. While many consumers grudgingly accept long waits in the car purchase process as the cost of doing business, this patience will not be permanent. In the UK, motor finance applications can take up to two working days to complete, according to MoneyExpert.com (see https://bit.ly/MoneyExpertdotcom). Some of the delay is caused by the ‘rehash procedure’ that the dealer has to conduct with their lenders to arrive at an acceptable offer.
The difference between the ‘before experience’ and the ‘after experience’ is the speed with which the finance and insurance provider in the back office was able to get a differentiated deal and approved counteroffer back into the hands of the salesman. Not just a long list of options to sift through, but a small set of meaningful options that the borrower will want to consider that also work for the lender and dealer. Plus, of course, any approved offer needs to conform to the lender’s credit risk criteria, which can be arduous to determine when the offer terms are constantly changing.
This problem cannot be comprehensively solved within a lender’s existing rules engine. What’s needed instead is mathematical optimisation – the analytic technology that can search through thousands of potential combinations to deliver the best possible counteroffers within the lender’s and dealer’s constraints and return those offers to the dealer in seconds.
These are the challenges that FICO set out to solve with a solution we call alternative deal structure optimisation (ADS). Optimisation solvers factor in credit and pricing logic plus behavioural analytics, such as the likelihood of response. This kind of optimisation can help car dealers improve the customer experience and underwriting levels. ADS allows dozens, hundreds, or even thousands of options to be evaluated, helping to ensure that the best alternative offers are discovered. It uses a dynamic search path controlled by business objectives and policy constraints, eliminating the need for lists of all possible options to be considered.
In the indirect auto space, ADS helps balance the competing needs of three parties: consumers, lenders and dealers. And, as long as car purchases are fulfilled by dealers, even online-only purchase/financing will involve some type of dealer commission. Some of the richness of ADS optimisation involves finding that balancing act: how do lenders keep dealers and customers happy while meeting internal profit/ volume/risk objectives?
The most common use of ADS is in generating counteroffers, whether prepopulating for underwriters (manual review) or returning automatically to dealers (conditional approvals). However, ADS has a variety of applications:
• Approval with options - Generate additional options when the initial deal structure is system-approved. • Inventory decisioning - Request for approved financing options on all available inventory at a dealer. • Pre-approval - Request for approval limits not linked to any specific vehicle. • Manual review - Generate alternatives for the underwriter before queuing for manual review. • Buyer self-service - Guide dealer/ customer24 on approved structures via the self-service portal.
REAL-TIME OPTIMISATION In developing ADS, we needed to create customer-level, real-time optimisation that enables personalised offer generation, rather than an offline, portfolio-level optimisation formulation, which is more common. To do this, we enabled the creation of a multi-objective business strategy through usage of two or more optimisation slots, where each optimisation slot is a unique mathematical formulation, and efficiently solves all optimisation slots within a strategy simultaneously.
Our ADS solution supports objective/constraint definitions for multiple metrics related to consumer needs (e.g., monthly payment amount, loan amount, interest rate, etc.), dealer needs (commission, backend allowance, etc.) and lender needs (e.g., customer-level risk level, customer-level profitability). The solution includes a simulation tool for a strategy manager to evaluate candidate strategies on large volumes of transactions (whether historical or manufactured).
SUCCESS STORY A large auto finance company in the US looked to ADS when it was forced to reject too many prospective buyers: buyers it believed could have been profitably financed if the deals had been structured properly.
The company wanted to ensure that the maximum number of customers walked out the doors of its dealerships with deals in hand. However, its credit analysts generated too many credit exceptions, which reduced profitability. That practice also created inconsistencies among individual dealerships, reducing risk manager monitoring effectiveness and putting the company at risk of predatory lending practice accusations.
In addition, manual deal financing processes were time-consuming: credit analysts had to pour over data caseby-case without necessarily arriving at the optimal deal. The company wanted to transfer the majority of deal structuring from its individual credit analysts to its centralised risk managers, who could structure deals to accommodate shifting parameters without sacrificing profitability. It also sought to offer a range of best options to prospective customers so that it could close more deals at a higher loan-to-value rate.
The auto finance company's new chief information officer proposed a fresh vision for structuring deals. By implementing FICO® Optimization Solution for Alternative Deal Structure, it could use the power and flexibility of the most advanced mathematical modelling environment with userfriendly visualisation and control to meet complex pricing challenges.
AN ALTERNATIVE DEAL STRUCTURE SYSTEM WITH REAL-TIME FLEXIBILITY When customers are ready to purchase vehicles, the FICO system quickly generates up to ten different deal structures, all of which are profitable for the company and conform to its lending policies. The credit analyst selects three of those ten deals - whichever fit the particular customer’s situation best - and negotiates with the customer within the framework of those deals.
Using the ADS system, the auto dealer has revamped its deal structuring processes and is experiencing multiple benefits. These include:
• Increased loan approval rates for better dealer and customer satisfaction
Because credit analysts focus on deal execution instead of creation, the company now can ensure that its deals all fall within acceptable ranges. This alternative deal structuring also means that dealerships can speed up their negotiations and make their pricing more attractive to customers.
• Reduced annual losses by up to $12 million
Alternative deal structures are helping convert missed opportunities into deals that maximise profitability. Having alternative structures means that the company can reduce loss ratios, based on improved loan-to-value rates, multiple payment lengths and specific terms. The annual losses were cut by as much as a further $12 million.
• Faster negotiations, lower labour costs
The company estimates that using the solution will trim two or three minutes of each financing application, which equates to a saving of up to 150hours every day. Because credit analysts will save time on every customer application, they can handle more deals, which means that the company can increase sales without adding credit analysts or paying overtime, an additional savings of up to an estimated $3 million every year.
• Greater transparency, reduced risk
Now that its risk managers make more uniform decisions about deal structures and monitor activities more effectively, the company faces less risk of audits and penalties. Risk managers also have the flexibility to alter policies based on market changes, current promotions, and the competitive landscape.
Those changes went into effect immediately, so the company can maintain a more consistent experience throughout its dealerships.
ADS provides self-service capabilities to both the dealer and customer, who are both looking for more insight and input over dealstructuring decisions.
ADS has widespread and immediate benefits. Crucially, it also improves overall time-to-decision and reduces the need for manual decisioning. This all boils down to an increase in approval and booking rates, helping motor dealers respond to the challenging market conditions. We believe this is a ground-breaking application for optimisation that has potential applications in other lending areas, including mortgage lending, personal loans and credit cards – anywhere where competition is fierce and a fast, profitable counteroffer can win the business.
Richard Cowley is a Principal Consultant for Analytics at FICO. He has worked within financial services, both on the banking side and as an analyst and consultant for more than 25 years. During his time at FICO he has led many analytics projects across the customer lifecycle, across multiple industries, and has developed and implemented successful optimisation solutions across EMEA.
