Embedding Digital Technologies to Transform Auto Finance Originations

Auto finance originations in 2018 are in decline, a trend that is likely to continue into 2019. Taking a view over the next 5 to 10 years, there are multiple macro and micro factors, which will reshape the auto finance industry. Trends such as increased car sharing, Uber and Lyft services, a growing percentage of leases and higher used car inventories, and a lower rate of car replacement will ultimately lead to stiff competition across the auto finance and lending life cycle.

There are significant opportunities for improving efficiencies using digital technology and analytics in the auto lending value chain. Many functions are still deeply entrenched in the traditional ways of originating deals. While many other industries have taken advantage of analytics, mobility, and artificial intelligence (AI) technologies to disrupt their business models, auto financing is still catching up.

The question is, how do auto finance lenders begin this transformation and embed digital and AI technologies into the very core of their loan origination operations?

While the dealer channel still remains the most vital source of business, customer buying behavior has been evolving, which may challenge this norm. Car buyers are looking for more control and convenience in their car buying and financing experience. The emergence of direct channels and manufacturer experimentation of going straight to the customer is a clear indication of a shift in practice, which is affecting car buying behavior.

To start their digital transformation, lenders need to realign their origination processes, which are tied to the changing personas of their target customers. As the foundation and first step toward creating a digital strategy, they need to identify the negatives as well as the positives faced by dealers and their end customers and build their process transformation around that.

The next challenge comes in implementing a reimagined dealer/end customer journey across their core originations processes. Most of the large, established auto lenders have operations that are saddled with pre-existing legacy technologies. This leaves them struggling to implement new processes to drive dealer/end customer satisfaction and ensure regulatory adherence. As a result, there is a need to introduce orchestrated layers or advanced workflows on top of the core legacy systems, which can help drive the reimagined processes as well as responsiveness to the dealer and end customer.

This orchestrated layer becomes the underlying technology of which auto lenders have the ability to add digital technologies, such as robotic process automation (RPA) and AI-based technologies like natural language processing and generation (NLP/G) for automating intensive manual processes. NLP/G technology can extract information from contract documents, validate the information, and identify exceptions, improving productivity by more than 50% and helping auto lenders significantly reduce the time to fund.

Auto lenders are also increasingly using alternative sources of data — especially in subprime segments — for making credit decisions. While most of the change in this area is being led by bureaus, these alternative data sources enable auto lenders to utilize machine learning algorithms to improve their overall auto decision rates.

Well-established industries have been disrupted by the likes of Amazon, Uber, Airbnb, Tesla, and more. These players have successfully leveraged technology and analytics to focus on their end-customer needs and in the process have disrupted the existing operating models in those industries. It is just a matter of time before similar disruptors apply digital technology and analytics transform auto financing.

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