Rethink Debt Collection with AI and ML

Lenders, collection agencies, law firms, and other organizations in financial services inherently operate with a certain level of risk. And when it comes to collections itself, a growing influx of new debt, digitally aware customers, more stringent laws, and transformative technologies continue to make the job of collection agencies increasingly complex. These concerns have only been amplified by the economic crisis caused by Covid-19. Government-backed and regulatory agencies have stepped up to provide more protection and safe harbor laws to protect consumers hurting from the current downturn. According to the IIF and a report by Bloomberg, the world debt reached a record $281 trillion at the end of 2020, surpassing even World War II, and is set to rise again in 2021.

As a result, companies that operate collection operations must adapt and shift their business strategy to remain competitive. Maintaining an agile business structure is now essential for such firms. Nowhere is this more true than in regards to collection methods. Traditional approaches that include snail mail, emails, and phone calls, largely characterized by a one-size-fits-all strategy, prove inefficient and antiquated. Not to mention, they also lead to poor customer experiences. However, with rapidly shifting technology, regulations, and demographics, the collections industry is changing like never before. Machine Learning (ML) and Artificial Intelligence (AI) can be leveraged to improve recovery while also addressing some of the other challenges that lenders face.

Use of AI and ML in the collection process

With advances in AI and ML in debt collection, like AI-powered call centers, collection agencies are able to overcome the limitations of antiquated database systems that are still in use. With a potential that allows evolving from glorified data entry to strategic decision making, AI can significantly uplift collections operations and streamline previously tedious tasks.

The primary focus of AI-based collections is on increasing efficiency and output. When contacting each consumer, AI-enabled systems can calculate the most efficient communication method. The software uses AI and ML tools to analyze and anticipate customer behavior. Infused with behavioral science, it helps to create a unique approach for every individual customer. But it doesn't end here, the technology guides a debt collection agency in assessing a customer, making accurate assumptions, thereby improving the productivity of the agents and their success rate in connecting with consumers.

With the help of data-driven machine learning techniques, the agents have more bandwidth for more creative endeavors along with actionable insights that help them determine the best step forward based on customer inputs. Furthermore, AI-based post-call analytical models can help firms measure agent performance to identify opportunities for coaching and ensure compliance. For example, in AI-powered collections and call centers, consumers can self-service through easy-to-use apps that are confirmed to follow the latest compliance guidelines.

How technology will continue to shape debt collection in the coming years

1. Identifying potential defaulters: Instead of applying numerical reasoning to generate an unbiased answer, outdated debt collection strategies rely mainly on human instinct and knowledge alone. ML shifts the paradigm by allowing for the proactive identification of at-risk accounts before they default on payments. AI-enabled automated debt collection software, which is capable of reading massive volumes of user data, can assist in the creation of an intuitive early warning system capable of comprehending consumer behavior and determining the likelihood of an account is delinquent. This early warning system enables lenders to concentrate their efforts on at-risk customers to keep their accounts from becoming delinquent in the first place. Lenders can also create sophisticated client profiles using AI and ML to determine which customers are likely to resolve delinquencies on their own and which need proactive intervention.

2. Optimizing customer experience: Lenders have traditionally relied on phone calls to help them handle payment issues. Over the previous decade, new communication channels have emerged, making the typical personal visit or phone contact obsolete. Lenders now have more options than ever to interact with borrowers. AI systems can recognize and implement the best mode of communication for different types of borrowers, allowing for optimum customer involvement. Cross-platform connectivity also enables the use of numerous communication channels, allowing the most effective strategy to be tailored to specific borrowers.

3. Leveraging technology to automate old processes: One of the largest roadblocks to improving collection efficiency is the use of outdated processes, which need dozens of clerical procedures to be repeated over and over again. These tasks are time-consuming and prone to human mistakes in the Age of Tech. And along with fighting this very repetitiveness, automation also:

• Boosts client satisfaction and agent productivity
• Reduces expenses through enhanced automation of the decision-making process
• Increases profitability and reduces overdue debt collection
• Controls risks and increases policy compliance

Both lenders and debtors can profit from AI and machine intelligence as debt collection becomes more modernized. As it stands, they are already capable of improving debt collection within companies. Its ability to leverage data, machine learning, and behavioral science to understand clients on a deeper and more intimate level is substantially advantageous. AI eliminates the need for guessing and human bias, and each step may be used to logically automate the process and establish a customer-centric strategy. Because of how AI has the ability to change how collections are handled, agencies have been able to improve customer experience and generate exponential value.

Author

Love Chopra, Principal Architect, Provana

As Principal Architect at Provana, Love reports directly to the Chief Technology Officer and is responsible for the company's solution architecture team, delivering design and engineering guidance for the Provana Platform Group. Previously, Love was Co-Founder and CTO of CareerGuide.com and an IT consultant at Crawford & Company. Passionate about creating powerful products and fostering a rich tech team environment, Love also leads Provana's Advanced Technology Group (ATG) program, which acts as an incubator for new ideas and R&D within the company. He earned a Bachelor of Engineering in Information Technology from Rajiv Gandhi Prodyogiki Vishwavidylaya and has continued his executive program in Innovation & IT Management from the Indian Institute of Management, Bangalore.

About Provana:

Founded in 2011 and headquartered in Chicago, IL, and based out of Noida, Provana is a SaaS platform that gives leaders control over process-intensive operations. We serve law firms, insurance companies, accounts receivable agencies, and networked enterprises in the US market that are tightly regulated by the CFPB and other authorities. Provana is built on decades of experience in machine learning and natural language processing and helps customers manage sensitive interactions, analyze unstructured data, process personal information, and ensure compliance. Provana is backed by an NYC-based Fintech PE, most recently raising funds in November 2020.