Debt Collection and Artificial Intelligence: Help or Hindrance?

Michelle Gilbert, Esquire

State of Debt Collection

Artificial intelligence (AI) continues to impact many business sectors, including debt collection.  The average financial burden per U.S. household averages a little over $100,000 per household which includes mortgages, car loans, student loans and credit cards.  One in four Americans have defaulted with this debt, which provides opportunity for the debt collection industry.

In particular, inflation, higher interest rates and depressed wages have recently led to increased debt, but collection agencies and attorneys also have been impacted by these same factors, namely, rising costs and staffing shortages.  Challenges in “traditional” debt collection include:

• Labor intensive:  phone calls, letters, records, administrative tasks, human errors
• Stressful and harassing for customers
• Not targeted:  does not account for underlying reasons for non-payment
• Regulatory compliance risk
• Vulnerable data management:  compromise of customer privacy
• Limited customization and scalability
• Lack of impartial and fair treatment

Fortunately, there appears to be solutions and strategies to assist the industry to succeed, though not without some downsides.

Use of AI in Financial Services as Precursor

More and more financial services customers use AI in their financial transactions.  Adoption in the financial sector bodes well for further implementation in debt management.  Over 98 million Americans interacted with bank chatbots, a number expected to reach 110.9 million by 2026.   Companies like Wells Fargo, Bank of America, J.P. Morgan Chase, American Express and Fidelity Investments, who collectively employ over 3.3 million support center specialists, are investing in technology to reduce costs and automate most customer requests, with an anticipated cost savings of $7.3 billion globally.

Specific industry uses, which allow staff to focus on complex matters, include:

• Automated notifications about payments, minimum due amounts, grace periods, payment methods, and how to set up online or recurring payments
• Automated reminders
• Personalized communications for customers

So how is the debt collection industry, including third-party collection companies, currently using AI?  According to the industry, sixty percent (60%) of collection firms say they are considering using AI tools, and they have been slow to adopt innovative technology.  “When you started a collections agency, you used to need a phone and a filing cabinet.  We’ve progressed from there, but we’re slow adopters,” Valerie Ingold, managing director of Commercial Collection Corp. in New York, and president of the International Association of Commercial Collectors, Inc. shared in an online Context publication.

Current Uses of AI in Debt Collection Industry

As of mid-2023, eleven percent (11%) of collection companies use AI as follows:

• 58% of these companies use AI to predict payment outcomes, like ability or willingness to pay
• 56% use AI to profile customers for appropriate workflow
• 46% use AI to anticipate customer behavior
• 47% use AI to determine best communication approach

But there is so much more opportunity in the industry to implement AI tools, as acknowledged by more than half of companies.  Specific internal applications include:

• Read, interpret analyze and handle inquiries about including invoices, payment delays, disputes, outstanding debts, payment receipts, address changes, etc.
• Validate and archive information about customers, such as account statements and credit reports
• Uses data on income, credit scores and net worth to make collection decisions
• Increase productivity while minimizing labor costs

External uses increase customer participation and satisfaction:

• Act as virtual customer assistant for simple tasks with escalation to human agents
• Detect patterns in financial behaviors
• Personalize communications based on customer preference
• Increase efficiency

Look to  the increased usage of AI in the industry, such that this group will continue to provide updates on this topic.

What Can Go Wrong?

“Change is the law of life.  And those who look only to the past or present are certain to miss the future.”  John F. Kennedy

And with change, there always is a downside, and AI in debt collection is no exception.  Debt collection is very personal, and interacting with chatbots can be detrimental to the effectiveness of the process, especially with sophisticated commercial parties who evade interaction.

AI is initially expensive and requires investment in fairly new technology and training.   The old saying, data in, data out, applies because poor or inadequate data compromises the effectiveness of AI solutions.  Compromise of personal information and misapplication of data resulting in bias have been identified as concerns.    Because AI algorithms utilize historical data, biases or prejudices can be applied towards a certain group of people as part of the processes.   Also, AI models are predictive, not definitive, and so regular auditing of systems with informed human oversighted is necessary to minimize these issues.

Best practices that the debt collection industry implement when implementing AI include:

• Begin with accurate data and implement data privacy and security controls
• Use transparency in AI decisions making and solicit feedback for stakeholders, employees, customers, and regulators in understanding the system
• Account for and audit AI decision making in order to address errors and adjust
• Add human oversight and ability to override AI decisions, especially potentially biased or prejudiced algorithms
• Ensure compliance with laws and regulations
• Implement ongoing monitoring and improvement

AI presents a transformative opportunity for the debt collection industry, offering efficiency, scalability, and improved customer engagement while addressing labor-intensive processes and regulatory risks. However, its adoption must be approached thoughtfully, balancing the benefit with the cost of technological advances, robust data management, and human oversight to ensure compliance and lift. It may be slow at first, but the industry can harness AI’s potential to revolutionize operations while maintaining fairness and trust.