Last week, the world’s largest auto finance event was held in Las Vegas. It brought together over 1000 participants – lenders, dealers, and software vendors – to discuss the state of the industry and cover current issues. As lenders and dealers are having to account for the new digitally empowered consumer, the way they lend, interact with, and service consumers is changing at a rapid rate. Consumers want varied origination terms and the option to finance vehicles over a lengthier period of time (some as long as 84 months). This passes additional risk on to lenders – which they have to balance against expected losses and increased compliance and regulations. Connected consumers of today are shopping and making decisions quickly, commonly not wanting to spend hours on a dealer lot waiting for financing to be approved. All of these factors create an environment where efficiency is valued, and in order to provide a superior customer experience – decisions have to be made virtually instantaneously based on available data.
So the question that came up at the conference was, “How are lenders and dealers accounting for this consumer change?” Let’s look at how technology is enabling this shift – particularly from a big data and predictive analytics perspective.
- Big Data Enables Innovation
Lenders today are dealing with more data than ever before. Internal consumer financial data, third party credit data, governance data, digital and social advertising and marketing data, and other data attributes – are hitting lenders at a rapid rate. Lenders have to have a process for aggregating and acting on data quickly. With emerging sources such as streaming data and telematics data from insurers and manufacturers coming onto the scene, the volume and variety of data will only continue to grow. This creates the need for a big data enabled environment that can work with millions of records in a simultaneous fashion. As we see cloud technologies becoming increasingly pervasive, big data processing is now becoming more feasible for any firm. From a cost perspective, big data infrastructures are today affordable even for the smaller lenders. From a security perspective, environments like Amazon Web Services are often much more secure than traditional on premise data warehouses. Because big data cloud based environments are now available, action can be taken more rapidly, even simultaneously by many departments in the organization. Through the sessions I attended, I repeatedly heard that departments like Finance, Risk, and Marketing are requesting more and more for the same all-round view of the consumer based on big data perspective. Getting this view requires software that enables data joining, cleansing, and categorizations that make sense for the business. New software technologies today enable rapid queries against large data volumes that determine risk exposure and then model that risk against potential profit margin – all while protecting assets and allowing lenders to operate in a fair, compliant fashion. This allows for analytics to then be applied in order to take action – whether it is quickly modifying origination terms, requesting additional consumer funding, or even declining a consumer.
- Emerging Analytic Techniques
This topic was very present throughout the conference and was even highlighted in one of the Earnix sponsored sessions around building analytics into auto finance organizations. For some time now, basic descriptive analytics (what happened) and diagnostic analytics (why did it happen) – have been used in the auto finance industry. Today, however, organizations are starting to embrace and use emerging predictive and prescriptive analytics. Predictive analytics looks at answering the question of “what will happen?” and prescriptive analytics answers “how can we make it happen?”. In order to answer the predictive and prescriptive questions, techniques such as forecasting, simulation, optimization, and machine learning are now being leveraged. Auto finance companies are leveraging forecasting to predict changes in seasonality, demand, and trends in vehicle purchase. Simulation is being used to model “what-if” scenarios, looking at how changing business or financial constraints, rules, and policies might affect performance of the asset portfolio. Optimization is being combined with real time decision making to determine the best prices and terms to offer consumers – down to the individual level – and then serve those pricing recommendations into a pricing environment. Machine Learning looks at how software systems can learn from data, identify patterns, and predict future results. Within auto finance, this takes the form of customer lifetime value calculations, new pricing model creation, credit scoring, next best offers, and the subsequent delivery of these offers into channel.
With 70- 75% of all consumers financing vehicles upon purchase, there is certainly a need for lenders and dealers to work together to continue to enhance the auto financing process as we know it today. Last week’s trip to Vegas confirmed for me that lenders and dealers are working together from a big data and predictive analytics perspective – which will allow them to continue to improve the customer experience that they deliver.
If you want to learn more about how all stakeholders in the auto finance industry can gain a strategic advantage through integrated customer analytics, please click here