Interview with Jeff DiMatteo, President of ACA International
In the United States, the adoption of artificial intelligence in debt collection appears to have accelerated rapidly. According to recent industry data from TransUnion, in 2025, 93% of companies reported that they were either using or exploring AI and machine learning solutions, compared with 73% in 2024.
We also learned, according to a report from Gartner, that more than 50% of customer service and support organizations are expected to double their technology investment by 2028.
Based on that and the TransUnion data the receivables management industry is already seeing a parallel trend in AI adoption compared to customer service and support organizations.
The shift reflects a move beyond experimental AI use cases toward the deep integration of generative AI and other automation tools into daily workflows.
AI is playing an increasingly important role in the receivables management industry. AI offers both operational efficiency and enhanced consumer engagement.
The topic is also explored in greater depth by ACA International in the article Companies Lean Into AI to Boost Human Productivity, which examines the growing role of artificial intelligence in enhancing productivity.
How deep is this adoption process really? What are the most common applications of AI among U.S. companies today, and what concrete results are you seeing in terms of productivity, costs, recovery rates and customer engagement?
For firms using AI, the primary dividend isn’t headcount reduction — it’s capacity expansion. Early adopters in professional services and knowledge sectors report significant productivity gains by automating routine and administrative tasks.
As hiring and retaining skilled staff becomes more challenging, companies are using technology to handle routine tasks, allowing human staff to focus on more nuanced consumer interactions.
At the same time, while AI can automate many processes, successful receivables management companies use a tailored approach, recognizing that some consumers still value the empathy and understanding that comes from human interaction.
Do you already have evidence that allows you to quantify the return on investment? For example, how much can a collector’s productivity increase, or a company’s recovery rate improve, when AI is effectively integrated into its processes?
Industry analysts point out that simply spending capital on new software does not guarantee a return on investment. The challenge lies in integrating these tools with legacy systems and ensuring that digital communication remains compliant with evolving standards.
In our industry, the clearest returns so far come from added capacity rather than cost-cutting. AI and automation can take on high-volume, routine work such as payment processing, basic account inquiries and call documentation, which frees experienced collectors to spend more time on complex accounts and on conversations with consumers that require judgment. Measured results vary widely by company, by type of portfolio and by how well the tools are integrated with existing systems and compliance controls, which is why most organizations are taking a phased, test-and-measure approach rather than making sweeping changes.
The topic is also explored in greater depth by ACA International in the article Companies Lean Into AI to Boost Human Productivity, which examines the growing role of artificial intelligence in boosting productivity and operational efficiency, with a particular focus on the opportunities offered by automation in the debt management and collection industry.
In your view, which areas of the credit industry will be most significantly transformed by AI over the next three to five years: customer communication, collections, credit scoring, fraud detection, compliance, back office or decision-making?
A Gartner survey of 321 customer service and support leaders, conducted in late 2025, showed that 58% of service leaders plan to upskill agents into knowledge management specialists, “acknowledging the need for accurate, continually updated content to support both AI systems and customer self-service interactions.”
In the receivables management space, this has led to a notable increase in the use of self-service portals and virtual negotiators. The TransUnion data shows that more than 98% of collection organizations now offer at least one self-service capability.
This trend aligns with Gartner’s prediction that consumers will increasingly favor digital-first interactions, provided they are seamless and effective.
Collections is an extension of the creditor community. In the creditor community, we’ve seen well-received use of AI in customer service functions. Similarly in collections, those activities surrounding the activities of customer service will benefit greatly from AI. This should result in a more positive consumer experience from faster response times and 24/7 access and servicing.
Collections, credit scoring (assessing a portfolio to identify consumers with the ability or capacity to pay), fraud detection, compliance, back-office activities and decision making should all benefit from the use of AI. AI has both speed and a broader level of analytics. This can drive more customized solutions at scale, which should yield improved results. In America, and prior to AI, the industry had done a decent job of adopting Robotic Process Automation (RPA) as a way of automating repetitive tasks — and that learning has in part set the stage for adoption of emerging AI tools.
Which activities, on the other hand, do you believe will inevitably remain human-driven?
When a dispute occurs or consumers have in-depth questions about their account, they need to speak with a collector who can help.
Human involvement will remain critical for handling consumer disputes, complex account inquiries, and high-stakes scenarios where automated systems might not have access to all the facts or lack necessary judgment. While generative AI can efficiently automate basic communication and routine questions, human collectors are essential to navigate strict compliance requirements, evaluate detailed documentation, manage sensitive tones, and resolve escalated issues that AI cannot effectively manage.
What do you see as the main obstacles currently preventing the credit industry from turning the potential of AI into tangible value for companies?
One obstacle is balancing the use of AI between back-end technologies and front-end services helping consumers. The work of human collectors is essential to help consumers in complex situations.
According to an AI-Driven Growth to 2030 study released by IBM, approximately 75% of surveyed executives believe that the competitive landscape of the next five years will be defined by how effectively organizations integrate generative AI into their core operations.
The IBM data indicates that 63% of CEOs are feeling increased pressure to accelerate their AI adoption to maintain pace with competitors. However, the report highlights a significant execution gap.
While 72% of organizations have initiated AI projects, only 15% have reached a level of maturity where these tools are fully integrated across departments.
In the receivables management industry, this lag often shows up in the work of designing automated consumer interactions that meet the requirements of the Fair Debt Collection Practices Act, Regulation F and other regulatory frameworks. Due to the significance and breadth of the regulatory frameworks, at both the federal and multi-state level, great care is exercised to ensure the technology honors those standards.
The cost of implementation remains a point of contention. While a PwC survey (https://www.pwc.com/gx/en/1/issues/c-suite-insights/ceo-survey.html) shows that 40% of CEOs expect AI to increase profitability by more than 5% in the next 12 months, the upfront capital expenditure for hardware and talent is significant.
The fact that AI can make mistakes and hallucinate will limit certain applications. There are plenty of opportunities being used today deploying AI and that will grow over time. However, it must be done with appropriate safeguards to ensure accurate information is being conveyed to consumers. A principle often associated with legendary American football coach Vince Lombardi is “flawless execution of the basics.” That notion will be more important than ever in using these evolving technologies.
From the consumer’s perspective, does AI risk making debt collection more impersonal, or can it actually contribute to making the relationship more personalized and responsive to each individual’s circumstances?
As companies in the receivables management industry strategize on how to move artificial intelligence from a back-office function to a front-line consumer touchpoint, some companies outside of our industry are discovering that the path to successful automation relies on how gracefully the technology steps aside for human intervention.
For businesses handling highly sensitive transactions — the stakes of automation are particularly high.
The topic is also addressed by ACA International in the article How Two Tech Companies Are Using AI for Customer Support, which explores how two technology companies are leveraging artificial intelligence to enhance customer support.
We believe consumers will appreciate the speed and service from AI tools being used to deal with straightforward inquiries and they’ll also appreciate the human touch when they’re involved in complex conversations and needing another person to listen to them and help them work out viable solutions.
Does AI represent primarily a new compliance challenge, or can it become a tool to strengthen and improve compliance monitoring and controls?
AI generally can enhance compliance in consumer communications by systematically monitoring interactions for adherence to legal and regulatory requirements.
For example, AI can analyze communications to ensure appropriate disclosures are made and that engagement remains respectful and within regulatory guidelines.
At the same time, a June 2026 report shared by ACA said regulators are asking detailed questions about banks’ AI controls, governance frameworks, protections for client data, third-party risk, vendor oversight, subcontractor exposure and contingency plans in the event of AI-related failures.
For ACA members working with financial institutions as clients, the report is another reminder that AI governance is becoming an increasingly important focus.
Companies using or evaluating AI tools should regularly review internal approval processes, data-use restrictions, vendor management practices and employee training to ensure AI is used responsibly and with appropriate oversight.
AI can help as a back-end tool for employees to use for strategy and compliance work, but human intervention is needed for consumer communications that involve complex accounts considering insurance, multiple debts, and a host of other variables.
How important is data quality to the success of AI in the credit industry? And how well prepared is the industry today, from a data perspective, to fully leverage its potential?
Data quality has always been foundational in our industry, and AI raises the stakes. Due to AI’s speed and scale the impact of any data error can be felt more quickly and in greater volume. The industry starts from a solid foundation: receivables management companies already operate under strict data accuracy, validation and dispute-handling requirements, including those in Regulation F, and many have invested heavily in data governance as part of their compliance management systems. The work ahead is ensuring that the data used to train, prompt or guide AI tools meets those same standards and is monitored continuously.
Federal Trade Commission Chairman Andrew Ferguson said companies should not treat AI agents as independent actors when assessing responsibility for their conduct, in remarks at a Reuters Momentum AI event on Sept. 25. His comments were a policy view rather than a formal rule, but they signal how regulators are likely to look at these systems.
As AI agents move from customer service applications to transactional capabilities — such as processing payments, altering account details, or issuing refunds — financial institutions must maintain clear documentation.
Regulators will evaluate governance controls, user permissions, and instructional logs when assessing how automated systems are used.
For the receivables management industry, where AI offers both operational efficiency and enhanced consumer engagement, it’s clear that instructions, permission boundaries, and audit logs surrounding these systems can carry the same regulatory exposure as human conduct.
Similar to you and the EU with Regulation EU 2024/1689 of the European Parliament and the Council, there are laws with both public enforcement and private rights of action available to consumers. AI-specific laws are still developing in America, but the underlying consumer protection laws already apply to how these tools are used. Said another way, the industry’s robust compliance management systems are built to evaluate evolving risks, including those from AI tools, and to put controls in place to prevent errors that could arise from poor design or poor execution of the new technologies.
How will the role of collectors evolve with the introduction of AI? Will we see a reduction in human involvement, or rather a transformation of the skills required from collection professionals?
Artificial intelligence is quickly changing the customer service workforce, but that does not necessarily mean people are being removed from the process entirely.
Instead, the work may look different.
Customer service jobs are expected to become more technical as AI takes on more routine inquiries, CX Dive said in a recent article based off a Forrester report.
Receivables management professionals are watching this closely as agencies evaluate how automation, compliance, consumer communication and escalation processes fit together.
Forrester predicts AI could reduce the customer service workforce by half by 2030, with the biggest impact on representatives who handle basic, easy-to-answer questions. But the report also makes clear that human involvement will still be needed, especially when issues become more complex, sensitive or require judgment.
This is especially relevant in the receivables management industry since consumer communication often requires more than a quick answer; agencies must consider compliance requirements, documentation, account details, creditor requirements, consumer disputes and the tone of the interaction. AI may support parts of that work, but human judgment remains critical when the stakes are higher.
AI may change who handles the first question, but people will still play a key role in how organizations respond when the answer is not simple.
The topic is also explored in greater depth by ACA International in the article AI Is Changing Customer Service Roles, Not Eliminating the Need for Human Judgment, which examines how artificial intelligence is transforming customer service roles without eliminating the need for human judgment.
We have had ACA members say from the stage at various events that the type of worker they need to hire or retain will evolve — ones that can understand the new technologies and navigating their application and functioning will become a larger proportion of the workforce.
In the United States, the debate around AI in the credit industry is evolving very rapidly. Looking at Europe and its more regulated approach to artificial intelligence, do you think the U.S. should move faster toward regulation, or does Europe risk regulating too early a technology that is still evolving?
What our industry needs most is consistency: a single, risk-based federal standard rather than the risk and complexity of a growing patchwork of state laws. A tailored and uniform approach to regulation that protects consumers without hindering the responsible use of technology is ideal. America currently has strong consumer-protection laws and those protections remain unchanged regardless of the technology used to serve those consumers.
Europe and the United States are taking different paths, and both share the goal of protecting consumers. For our industry, the priority is a consistent, risk-based framework that builds on the strong consumer protection laws already in place.
FTC Chairman Andrew Ferguson has also suggested looking first to existing laws before writing new ones, and consumer protection laws such as the Fair Debt Collection Practices Act already govern consumer protections in our industry.
ACA International remains a vocal advocate for a tailored regulatory framework, pushing for state AI legislation that aligns with a consistent national framework to prevent a patchwork of rules.
As part of that effort, ACA leaders have developed Guidelines and Principles for Artificial Intelligence Policy, stating that any framework to regulate the use of AI should be risk-based and focus on mitigating potential harm to consumers, not back-end processes that allow businesses to run more efficiently.
ACA International outlined the need for a tailored approach to artificial intelligence regulations and the technology’s uses in the receivables management industry in recent comments on the Office of Science and Technology Policy development of an Artificial Intelligence Action Plan.
ACA has urged Congress and federal regulators to recognize AI as essential for consumers’ flexibility in managing account information and payments, emphasizing that national AI priorities should reflect these benefits for consumers and businesses.
