Consumer debt comes in many forms, from mortgages and car loans to credit cards, overdrafts, and buy-now-pay-later schemes. Behind these financial products are personal stories, with debt being a deeply human experience that often involves stress, shame, and vulnerability. Financial institutions (FIs), on the other hand, face significant challenges in managing debt portfolios, balancing their regulatory obligations with economic pressures and shareholder expectations.

The regulatory environment is rigorous, with frameworks like IFRS 9 influencing provisioning for bad debts and the FCA’s Consumer Duty requiring firms to ensure fair outcomes for customers. AI has introduced tools such as open banking and digital automation to enhance efficiency, it also raises questions about the balance between technological capabilities and preserving the human element of debt collection.

By the end of H1 of 2024, UK household debt reached a staggering £2,1 trillion, underscoring the scale of the issue. With over half of UK adults using some form of credit, managing debt has become a crucial focus for both individuals and the institutions serving them. Yet, many consumers still report dissatisfaction with how FIs approach debt collection, citing concerns around fairness, communication, and support.

Types of AI

AI is not a monolithic technology; different types of AI serve distinct purposes. Understanding their maturity and potential use cases is critical for effectively deploying AI in debt collection.

Conversational AI: This technology enables machines to understand and respond to human language via chatbots or virtual assistants.

Use cases in collections:

• Supporting self-service debt management through digital channels.
• Assisting customer service agents with real-time scripting prompts.
• Streamlining HR self-service and training for collections staff.

Generative AI (GenAI): Generative AI models create content such as text, images, or videos based on prompts.

Use cases in collections:

• Transcribing and summarising agent-customer conversations.
• Suggesting next-best actions to advisors.
• Automating the generation of regulatory or legal reports.
• Enhancing chatbot capabilities.

Automated Workplace Assistants (AWAs): AWAs use robotic process automation (RPA) combined with AI to handle repetitive tasks and assist with complex ones.

Use cases in collections:

• Automating compliance and complaints handling.
• Collating and organising documents for litigation support.
• Providing virtual assistants for customer debt self-service.

Explainable AI (XAI) XAI focuses on transparency, ensuring that AI-driven decisions are understandable and free from bias.

Use cases in collections:

• Improving credit decision transparency.
• Ensuring fairness in repayment plan recommendations.
• Resolving customer complaints more effectively.
• Increasing confidence in Treating Customers Fairly (TCF) processes.

TuringBots: TuringBots assist software development teams by automating code generation, testing, and documentation. While still in their infancy, they have the potential to transform the way FIs develop and deploy software solutions.

Use cases in collections:

• Generating and documenting application code.
• Creating automated test cases for debt management platforms.
• Rapidly prototyping user interfaces.

Where AI Fits in the Debt Lifecycle

The effective use of AI can transform debt management, enabling earlier intervention and better outcomes for both FIs and consumers. By identifying patterns and predicting defaults, AI helps firms avoid costly late-stage litigation. However, its application must consider the diverse needs of debtors, and the type of debt involved.

AI’s role is not to replace human agents but to augment their capabilities, ensuring that customers receive tailored, empathetic support.

Keeping It Personal

Despite AI’s potential, human factors remain critical in debt management. FIs must tread carefully, ensuring that:

• Customers always have the option to speak to a human advisor.
• Personal circumstances, such as financial distress or mental health issues, are identified and addressed promptly.
• Ethical considerations guide AI-driven repayment options, ensuring alignment with FCA regulations.
• Digital channels are accessible and trustworthy, particularly as open banking adoption remains low.

For example, an AI system might predict a customer’s likelihood of default, but the subsequent engagement should be sensitive to their situation. Directing distressed customers to organisations like StepChange or PayPlan can demonstrate genuine care while fostering trust.

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Financial institutions need to balance their duties to shareholders in the management of debt with the human aspects of debt and the regulatory environment.

It is evident that AI is here to stay, and its role in debt collection and management will only expand. However, financial institutions must remain committed to ethical practices and customer-centric approaches to maintain trust. The most successful deployments of AI will be those that enhance human expertise rather than replace it, ensuring that consumers receive the right support and guidance while also benefiting from the efficiencies AI can provide.

By integrating AI responsibly, organisations can create a future where technology supports, rather than overrides, the human elements of debt management. Ultimately, AI should serve as an enabler of better financial outcomes for both institutions and their customers, fostering a more responsible and effective approach to debt management.

To learn more about SBS Collection solution, please visit our website.

Jason Gouk, Strategic Sales Director, SBS