Personalized Digital Experiences Drive Engagement and Liquidity for Consumers

Debt collection has existed for as long as consumers have been taking loans. For the past few decades, collectors have been building call center businesses - hundreds and thousands of calling agents, using automated dialers to contact indebted consumers, compensated with commission once they reach their collection goals. Consumers are often harassed by overzealous collectors looking to meet their goals, calling as much as 6 times per day. It’s a stressful environment focused on one thing - get the money or get out. 

In the past few years, collectors have seen a shift in consumer behavior, and the old way of collecting is becoming less relevant and effective. Mobile and digital experiences have changed how we interact and communicate, and consumers are demanding the same from collections. Reply rates to letters have plummeted, as well as the number of consumers who pick up the phone. Some issuers report double-digit growth in the percent of consumers who are strictly digitally engaged.  By 2018, according to Gartner, consumers will consume more than 50% of content on mobile devices, and expect communication by email and text. At the same time, more consumers are taking loans and end up in collections (several banks have reported a surge in default rates above 5% in the past two quarters), and communicating with all of them at scale, and while staying compliant is impossible. Burdened by thin margins and a legacy call center approach, collection agencies have failed to make the deep investment required to use modern, integrated technologies and adapt to consumer needs. This, along with ongoing changes in regulation, is why collectors’ success rates are decreasing while litigation numbers keep rising. 

In contrast, machine learning and digital first systems are emerging to deliver great consumer experiences at scale and with better results. Developed with the most modern tools, these systems offer the best possible user experience, a personalized communication, and enable a strategic, data-driven approach to collections. Using their advantage in efficiency, scale, performance and compliance, machine learning based systems are delivering a better alternative to call center based collections. Whenever the human-intensive and the machine learning based approach square off, these modern tools provide a superior result.