Preparing for AI in collections and recoveries

Building the foundations for AI in collections and recoveries

Before investing in technology, organisations should focus on building awareness, operational maturity and governance structures that enable AI to deliver value safely.

Developing organisational awareness

AI is widely discussed but often poorly understood. Within collections operations this can lead to two common problems: unrealistic expectations or excessive caution.

Developing a shared organisational understanding of AI is an essential first step. This means helping stakeholders understand:

• What AI actually is and how it differs from traditional analytics

The types of AI capabilities relevant to collections operations

Where AI can support decision-making rather than replace human judgement

The limitations and risks associated with AI models

This awareness needs to extend beyond technology teams. Operations leaders, compliance teams, customer strategy teams and senior management all need to understand how AI could influence collections strategies and customer outcomes.

Without this shared understanding, organisations often struggle to identify realistic use cases or make informed investment decisions.

Assessing organisational maturity

AI works best in organisations where operational processes are already well understood and well controlled. Where processes are inconsistent or poorly defined, AI can simply automate existing problems rather than solve them.

Before introducing AI, organisations should review their maturity in several key areas:

Data quality and accessibility

AI models rely heavily on historical data. Poor quality or fragmented data will significantly limit model effectiveness.

Collections strategy design

Clear strategies and segmentation approaches provide a stronger foundation for AI-driven optimisation.

Process consistency

Standardised processes enable AI to support decision-making more effectively.

Management information and performance monitoring

Organisations need clear metrics and monitoring frameworks to understand whether AI-enabled decisions are improving outcomes.

AI adoption should therefore be viewed as part of a broader operational maturity journey rather than a standalone technology project.

Ensuring accessibility to real operational issues

Another common mistake is beginning AI programmes with technology experimentation rather than operational need.

Successful initiatives start by identifying the problems that genuinely matter within collections operations, for example:

Where existing data is underused

The most valuable insights into these issues often sit with frontline teams, strategy managers and operational leaders rather than technology teams.

Organisations that involve these groups early in AI discussions are far more likely to identify practical and impactful use cases.

Understanding and managing risk

Collections and recoveries operates within a highly regulated environment. Introducing AI into decision-making raises important questions about fairness, transparency and governance.

Organisations should ensure they have appropriate frameworks in place to address issues such as:

Fair customer outcomes

AI-driven decisions must not disadvantage vulnerable customers or lead to unfair treatment.

Explainability

Firms must be able to understand and explain how AI-supported decisions are made.

Model governance

AI models require structured validation, testing and ongoing monitoring.

Operational resilience

Organisations must ensure AI does not introduce new operational dependencies or failure risks.

Addressing these risks early allows organisations to innovate confidently while maintaining regulatory compliance.

Building cross-functional capability

AI adoption is not purely a technology initiative. It requires collaboration across a range of organisational functions including:

Customer experience teams

Cross-functional collaboration helps ensure AI initiatives reflect both operational realities and regulatory expectations.

In many organisations, establishing small multidisciplinary teams to explore AI opportunities can be an effective way to build capability while maintaining appropriate oversight.

Starting with responsible experimentation

AI adoption does not need to begin with large-scale transformation programmes. In many cases, organisations benefit from starting with smaller pilot initiatives.

Controlled experimentation allows organisations to:

Build internal confidence in AI capabilities

These early initiatives can provide valuable learning while limiting risk and investment.

Preparing for the future of collections

AI will play an increasing role in collections and recoveries over the coming years. The organisations that benefit most will not be those that adopt the technology first, but those that prepare for it most effectively. Focus on organisational awareness, operational maturity, clear governance and practical use cases, and AI becomes a tool for better decisions, tighter operations and better customer outcomes. The question is no longer whether AI will influence collections and recoveries. It is whether organisations are ready to use it well.

How arum can help

We work with organisations across financial services, utilities, telecoms, government and technology to improve collections and recoveries. On AI, that support usually takes one of five shapes.

• AI readiness assessments: organisational maturity, governance frameworks and data capability.
• Collections strategy and operating model design: the foundation AI-enabled decisions need.
• Risk and governance frameworks: model governance, oversight and customer fairness.
• Use case identification: practical opportunities that match operational objectives and customer outcomes.
• Technology evaluation: assessing AI solutions within the wider collections technology landscape.