Auto finance is a segment that has been on the decline. And that trend will continue in 2018 and likely will continue in 2019 as well.

Looking at the market over the next five or ten years, there are several micro and macro factors that will reshape the auto finance segment. The trends that are on the rise are car sharing services like Lyft and Uber services and a growing percentage of leases and car inventories are being used by such services, thus lowering the rate of replacement of cars. These factors are creating stiff competition between the lending life cycle and auto finance.

There are several opportunities that emerge which showcase efficiency improvement with analytics and digital technology. These are becoming part of the value chain in auto lending. Many functions are deeply entrenched in ways of how deals originate or how they are traditionally done. Several industries take the advantage of mobility, artificial intelligence and analytics in order to bring disruption in the business models. Auto financing still in the mode to catch up to that trend.

The questions that arise are: How will auto finance lenders work on this transformation? How will it use the embedded AI and digital technologies to bring about changes in their existing ways of operation with auto financing? And, How do loan originations happen?

Dealer channels are a vital source in such a business; customer buying behavior is evolving and this poses a challenge to the existing norm. Car buyers would look for control as well as convenience in the auto financing and buying experience. As direct channels emerge and manufacturers experiment with going straight to the customer, there comes a clear shift in practices. And this, in turn, will affect the buying behavior of cars.

In order to bring about digital transformation lenders need to realign their processes of origination, which is linked to the changing personas of the customers they target. A good foundation will help to create digital strategies that could identify the negatives as well as positives faced by auto dealers as well as end customers. This, in turn, will help build process transformations.

Challenges can emerge when the journey of a dealer and end customer journey is reimagined, so a good foundation needs to be implemented across the different core processes that exist. Several large and established auto lenders have operations based on pre-existing legacy technologies. This brings about struggles in implementing different processes, which will drive customer satisfaction as well as ensure that regulations are adhered to. There is a need to have several layers orchestrated as well as advanced workflows which are added onto the legacy systems at the core. This helps drive different processes, as well as brings about responsiveness in the dynamic between dealer and customer.

The orchestrated layer is what underlying technology helps so that auto lenders can take advantage of digital technologies like robotic process automation or RPA as well as AI-based technologies where generation as well as natural language processing technologies can help automate the manual processes. With the help of NLP/G it is possible to automate manual and intensive processes. The technology can help to extract information existing in contract documents, validate the necessary information and identify the exceptions. This will also help to improve productivity by more than 50% as well as help auto-lenders, who can significantly reduce their funding time.

Auto lenders can increasingly use alternative data sources, which is pertinent for subprime segments and helps to make credit decisions. Most changes in the area are led by the bureaus; alternative sources of data will enable the auto lenders who can make better auto decision rates by utilizing machine learning algorithms.

Industries that are well established have used such technology to change their processes such as Tesla, AirBnB, Uber, Amazon and others. These companies are able to leverage technology as well as analytics so that they can focus on end customers and their needs. Similar transformations will come by in analytics and digital technology.