The volume of debt managed by firms employing advanced artificial intelligence (AI) solutions has surged, with 91% of the financial sector now integrating or actively testing AI in their operations as of early 2025, according to data from TransUnion. This represents a dramatic increase from just 11% in 2023 and 18% in 2024.
This shift isn’t a fleeting trend, but a response to escalating consumer debt – U.S. Household debt exceeded a record $18 trillion in 2025 – and a pressing need for operational efficiency. McKinsey & Company data indicates that investments in these technologies can reduce operational costs by as much as 40%.
A key development driving this transformation is the adoption of predictive analytics, allowing companies to move away from less effective, high-volume calling methods. By leveraging AI algorithms, firms specializing in debt management can analyze vast datasets in real-time, encompassing historical payment patterns and digital consumer behavior.
“These are still very fresh tools, and their implementation and utilize within our company are currently in the early stages, so I wouldn’t dare draw definitive conclusions,” says Marius Šlepetis, head of technology at debt management firm Legal Balance. “Though, our calculations suggest that applying predictive analytics will increase the efficiency of segmentation and communication with debtors by around 20%, and similarly increase the amount of money collected from clients.”
Intelligent debtor segmentation enables market leaders to more accurately determine which clients require only an automated reminder and which necessitate direct engagement from a manager. According to financial services management consultancy Bridgeforce, optimizing these processes significantly improves debt recovery rates while reducing operational costs by 30 to 40%. This allows staff to focus their time on cases requiring human intervention.
Modern technology is as well enhancing the quality of communication with debtors. Firms are increasingly utilizing voice and text analysis tools – known as sentiment analysis – to identify a client’s emotional state or keywords indicating financial vulnerability, such as job loss or health issues. Proactive analysis allows for the automatic adjustment of communication tone or the offering of flexible payment plans before a situation escalates.
“A ‘human’ approach and AI involvement at the same time may sound paradoxical, but in the debt management field, it works perfectly: customer satisfaction grows, creditors are assured of greater value, and problems are resolved more quickly,” Šlepetis stated.
The modern economic landscape is characterized by a complex web of financial obligations, where debtors and creditors collaborate, and the problem often lies not in the debts themselves, but in their management. Šlepetis notes that debt continues to carry a stigma, often equated with illness or trauma, hindering both problem identification and resolution. New technologies are helping to mitigate these risks.
The technological transformation is also driving a shift towards digital self-service channels, better aligned with the needs of contemporary consumers. Industry associations report a growing preference for digital communication and self-service portals, with direct phone calls often perceived as psychologically uncomfortable. Automation ensures service availability 24/7, allowing debtors to manage their finances privately and conveniently, while reducing the administrative burden on debt management firms.
The adoption of AI is projected to generate approximately 40% of all market revenue by 2034. Companies investing now in the accuracy of AI models and personalized customer experiences are poised to achieve superior financial results and contribute to a more sustainable financial ecosystem.
