Times are tough for auto loan companies.
Even before the pandemic hit the world, auto loan companies had started facing challenges. Customers were missing their auto loan payments.
According to the New York Fed’s Household Debt and Credit Report for Q4 2019, the outstanding vehicle debt had increased by 68% compared to Q4 2008. The situation further worsened during the pandemic when mass lay-offs took place, and customers defaulted on their payments. Over 7% of car loans are under the deferment program. To add to the woes, auto sales have dropped too. These developments have impacted auto loan companies severely. The road to recovery will take time as several countries are still reeling under the pandemic.
While not much can be done about the external conditions, auto loan companies can use advanced technologies such as data analytics, artificial intelligence (AI), and machine learning (ML) to prevent defaults and build better relations with customers.
How technology can help avoid defaults and build customer relationships
Revise loan structures
Auto loan companies have access to a goldmine called data. They have all the vital information about the customer, such as their credit score, their financial status, payment history, etc. Companies can use this data to determine if the customer is eligible for a loan. They can also customize it based on the customer’s financial profile to reduce defaults. Data analytics can also help predict chances of defaults in the future and reach out to the customer proactively to review and revise their existing loan structures. This will help reduce defaults and nudge the customers to pay on time.
Predict frauds
Although auto loan companies have automated the lending process, they still have a long way to go to predict and prevent fraud. In 2019, over 300 auto loans worth $5.5 million turned out to be fraudulent. The most common types of fraud include identity theft, incorrect information in documents, and stealing social security numbers to create new credit profiles. Data analytics, AI, ML can help in curbing these fraudulent activities. For example, ML can help companies analyze millions of loan applications to find out the potential for fraud. Analytics can help analyze the customer’s historical records and current documents to detect anomalies.
Improve underwriting process
There are often delays in auto loans approval due to ineffective underwriting processes. Auto loan companies can use analytics, AI, and ML to accelerate the process. They can find out potential risk sources and find ways to eliminate them. AI, ML can also improve the underwriting process. Take the example of Prestige Financial Services. They used to turn down 70% of the applications. They were able to increase the number of new applicants to 36% and avoid risks by using AI and ML in the underwriting process. They used ML to rank-order the customers based on their risk profile and replace the ones with high-risk with new-to-credit customers who are more creditworthy.
Streamline the lending process
The lending process was traditionally a labor-intensive process and prone to errors. ML can help streamline these processes. Whether it’s approving the loan applications based on the customer’s credit score, offering personalized loan products and payment options, or changing the debt recovery process, ML has made these processes more efficient. It has helped auto loan companies accurately identify the applicants capable of paying on time and approve their loan applications to minimize defaults. More importantly, it has made it convenient for creditworthy customers to quickly apply to different loan products and get approvals.
Improve customer retention
Auto loan companies often get a bad rep due to their rigid loan structures and complex lending processes. With auto sales plummeting and competition from fintech companies, auto loan companies need to find ways to retain customers. Companies can use predictive analytics to identify customers who display signs of defaulting and reach out to them to discuss tailor-made loan structures. They can also offer a self-service option to let customers choose their loan structures. Such personalized options will help customers engage with the loan company better and improve customer retention.
Conclusion
In an uncertain environment, earning customers’ goodwill is vital. Technologies like AI, ML, and data analytics can help auto loan companies personalize communication, restructure loan plans, and extend the terms for customers who cannot pay their loans on time. It’s time that auto loan companies harness these technologies and take a digital-first approach to reimagine their business, prevent losses, and provide better experiences for customers. However, this would require a complete overhaul of processes, systems, and even culture. Companies must work with trusted technology partners to transition from legacy systems and processes to modernized ones.
At Ascentt, we help auto loan companies to embrace advanced technologies such as data analytics to improve their business outcomes.
To know more, contact us.