Rethinking Debt Collection with AI and ML


by Analytics Insight

August 26, 2021

Lenders, collection agencies, law firms and other financial services organizations inherently operate with a certain level of risk. And when it comes to collection itself, a growing influx of new debt, digitally aware clients, tougher laws and transformative technologies continue to make the work of collection agencies increasingly complex. These concerns were only amplified by the economic crisis caused by the Covid-19. Regulatory and government-backed bodies have stepped up to offer more protection and safe harbor laws to protect consumers affected by the current crisis. According to the IIR and a Bloomberg report, global debt hit a record high of $ 281 trillion at the end of 2020, even surpassing World War II, and is expected to rise again in 2021.

Therefore, companies that operate collection operations must adapt and change their business strategy to remain competitive. Maintaining an agile business structure is now essential for these companies. Nowhere is this more true than with regard to collection methods. Traditional approaches that include postal mail, email and phone calls, largely characterized by a single strategy, are proving ineffective and obsolete. Not to mention, they also lead to bad customer experiences. However, with rapidly changing technology, regulations and demographics, the collections industry is changing like never before. Machine learning (ML) and artificial intelligence (AI) can be harnessed to improve recovery while addressing some of the other challenges that lenders face.

Use of AI and ML in the collection process

With the advancement of AI and ML in debt collection, like AI-powered call centers, collection agencies are able to overcome the limitations of outdated database systems that are still in existence. during use. With the potential to move from glorified data entry to strategic decision-making, AI can dramatically increase collection operations and streamline previously tedious tasks.

The main goal of AI-based collections is to increase efficiency and production. When contacting each consumer, AI-enabled systems can calculate the most effective method of communication. The software uses AI and ML tools to analyze and anticipate customer behavior. Infused with behavioral science, it helps create a unique approach for each client. But it doesn’t stop there, technology guides a debt collection agency in assessing a client, making precise assumptions, thus improving agent productivity and their success rate in connecting with consumers. .

With the help of data-driven machine learning techniques, agents have more bandwidth for more creative efforts as well as actionable insights that help them determine the best step based on customer input. Additionally, AI-powered post-call analytics models can help companies measure agent performance to identify coaching opportunities and ensure compliance. For example, in call centers and AI-powered call centers, consumers can self-serve through easy-to-use apps that are confirmed to follow the latest compliance guidelines.

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

1. Identify potential defaults: Instead of applying numerical reasoning to generate an unbiased response, outdated debt collection strategies rely primarily on human instinct and knowledge. ML changes the paradigm by enabling the proactive identification of risky accounts before they go into default. AI-based automated debt collection software capable of reading huge volumes of user data can help create an intuitive early warning system that can understand consumer behavior and determine likelihood that an account is overdue. This early warning system allows lenders to focus their efforts on at-risk customers to prevent their accounts from becoming delinquent. Lenders can also create sophisticated customer profiles using AI and ML to determine which customers are likely to resolve defaults on their own and which require proactive intervention.

2. Optimize the customer experience: Lenders have traditionally relied on phone calls to help them manage payment problems. Over the past decade, new channels of communication have emerged, rendering the typical personal visit or telephone contact obsolete. Lenders now have more options than ever before for interacting with borrowers. AI systems can recognize and implement the best mode of communication for different types of borrowers, enabling optimal customer engagement. Cross-platform connectivity also allows multiple communication channels to be used, allowing the most effective strategy to be tailored to specific borrowers.

3. Leverage technology to automate old processes: One of the main obstacles to improving collection efficiency is the use of outdated processes, which require dozens of administrative procedures to be repeated indefinitely. These tasks are time consuming and prone to human error in the age of technology. And in addition to fighting against this very repetitiveness, automation:

  • Increase customer satisfaction and agent productivity
  • Reduces expenses through improved automation of the decision-making process
  • Increase profitability and reduce bad debt collection
  • Controls risk and increases policy compliance

Lenders and debtors can take advantage of artificial intelligence and artificial intelligence as debt collection modernizes. 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 customers on a deeper and more intimate level is of considerable benefit. AI eliminates the need for guesswork and human bias, and each step can be used to logically automate the process and establish a customer-centric strategy. Because of AI’s ability to change the way collections are managed, agencies have been able to improve the customer experience and drive exponential value.


Love Chopra, Principal Architect, Provana

As Principal Architect at Provana, Love reports directly to the CTO and is responsible for the company’s solution architecture team, providing design and engineering advice for the Provana Platform group. Previously, Love was co-founder and CTO of and IT consultant at Crawford & Company. Passionate about creating powerful products and fostering a rich technology 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 business. He obtained a Bachelor of Engineering in Information Technology from Rajiv Gandhi Prodyogiki Vishwavidylaya and continued his Executive Program in IT Innovation and Management from the Indian Institute of Management in Bangalore.

About Provana:

Founded in 2011 and headquartered in Chicago, Illinois, and headquartered in Noida, Provana is a SaaS platform that empowers executives to control process-intensive operations. We serve law firms, insurance companies, accounts receivable agencies and network businesses in the US market that are strictly regulated by the CFPB and other authorities. Provana draws 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 a New York-based Fintech PE, most recently raising funds in November 2020.

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