Machine learning is already here – and you probably use it every day!
The major example here is Amazon. Years ago, Amazon developed a recommendation engine to suggest products to customers based on their purchase history and other factors. And clearly, it works – who hasn’t impulse-bought something that catches their eye in “Top picks for you”? According to McKinsey, up to 35% of Amazon’s sales come from its product recommendation system creating effective, personalized experiences.
Streaming services like Netflix also provide us with personalized views of movies and tv shows we might like using machine learning and algorithms, and Google uses machine learning in its search engine (Google Autocomplete) and advertising (responsive search ads) to show us products based on our interests.
As with retail, the goal of an auto lender should be to create streamlined customer experiences that are personalized wherever possible. As a starting point, you can use ML to enhance your credit decisioning and underwriting capabilities, verify borrower-provided documents, and prioritize workflows.
Credit underwriting and decisioning
In auto lending, there’s a balance to be struck between having a high approval rate behind your brand and making sure you only approve the most creditworthy customers. No customer likes being rejected, and auto lending is a data-intensive industry – so the goal of any lender should have as much data at their disposal in making credit decisions and analyzing risk. Some providers, for example, Zest. ai, that can use ML to analyze a lender’s portfolio and then build an ML-based scoring model for credit decisioning.
ML takes the pressure off manually trying to handle this volume of information and can automatically assess anything from risk levels to recommended pricing to likely resale values. Automated machine learning is one of the powerful tools in a lender’s arsenal for maximizing underwriting efficiency, as it can analyze historical data to develop predictive risk models for future use. As well as reducing costs by cutting the amount of human attention needed in repetitive data-checking tasks, it also improves consistency and accuracy by taking all data sources into account – ML can analyze a volume of data simply not possible with rules-based underwriting.
Traditionally, a lender’s funding team (or an outsourced function) manually examines borrower-provided documents, such as paystubs and driver’s licenses, to perform data entry and verify authenticity.
As you have probably experienced, this can take a lot of time, is subject to human judgment, and is prone to errors. Machine learning can be used to digitize this process and help develop predictive models that can form accurate estimates for qualification purposes. This is incredibly helpful for dealers, as the funding process is completed more quickly. As many applicants are likely to be comparing offers as and when they are received, the speed that they can receive this information is a real competitive factor.
Auto lending has a great deal of steps, which can sometimes feel like a never-ending back and forth between lender, auto dealer, and borrower. Without a set workflow, much like a meeting with no agenda, it’s easy to lose valuable time and action, as well as increase the likelihood of mistakes being made along the way.
ML can be used to develop optimized workflow models that can automatically assign the best action or step in the application process. This automated prioritization can dramatically affect the response rates in the application processes – which in turn means deals close faster. It can also improve consistency across all your applications processing, ensure you’re on top of the deals that need urgent attention, and improve the customer experience.
The path to machine learning nirvana
As a lender, if you’re looking to start using ML, the most straightforward path forward is to partner with a software vendor that specializes in it. You’ll benefit from both the technology and their ML know-how.
At Solifi, we use top-tier third-party integrations to make implementing ML into your operations faster and easier. Get in touch today to find out how we can help you take your next step into the world of machine learning!