Artificial Intelligence is rapidly moving from concept to practical application across financial services. In collections and recoveries, AI is already beginning to influence how organisations prioritise accounts, interact with customers, and design strategies.
However, the most successful organisations are not necessarily those that adopt AI the fastest. They are the ones that prepare for it properly.
Too often, AI initiatives begin with a technology conversation:
Which tools should we buy?
How quickly can we deploy AI?
In practice, the organisations that achieve real value start with a different question:
Are we organisationally ready to use AI responsibly and effectively?
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 manual effort is highest
- Where decision making is most complex
- Where customer outcomes are most sensitive
- 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:
- Operations and collections strategy
- Risk and compliance
- Data and analytics
- Technology
- 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:
- Test potential use cases
- Understand operational impacts
- Develop governance approaches
- Build internal confidence in AI capabilities
These early initiatives can provide valuable learning while limiting risk and investment.
How Arum Can Help
Arum works with organisations across financial services, utilities, telecoms, Government and the technology sector to improve collections and recoveries
operations. As AI capabilities evolve, many organisations are seeking independent support to understand how these technologies can be used responsibly and effectively.
Arum can support organisations at several stages of the AI journey:
AI readiness assessments
Reviewing organisational maturity, governance frameworks and data capabilities to determine readiness for AI adoption.
Collections strategy and operating model design
Ensuring that operational structures and strategies provide the right foundation for AI-enabled decision making.
Risk and governance frameworks
Designing oversight approaches that address regulatory expectations around AI, including model governance and customer fairness.
Use case identification and prioritisation
Helping organisations identify practical AI opportunities that align with operational objectives and customer outcomes.
Technology evaluation and vendor assessment
Supporting organisations in assessing AI solutions within the collections technology landscape.
By focusing on both operational effectiveness and regulatory compliance, Arum helps organisations adopt AI in a way that delivers real value while maintaining responsible customer treatment.
Preparing For The Future Of Collections
AI will undoubtedly play an increasing role in collections and recoveries over the coming years. The organisations that benefit most will not necessarily be those that adopt the technology first, but those that prepare for it most effectively.
By focusing on organisational awareness, operational maturity, clear governance and practical use cases, firms can ensure AI becomes a valuable tool for improving decision making, enhancing operational efficiency and delivering better customer outcomes.
The question is no longer whether AI will influence collections and recoveries.
The real question is whether organisations are ready to use it well.
Contact us for a free no-obligation call, and we'll help you answer that question.
About the author
Forid joined Arum in 2019 as a senior consultant and is now our Head of Global Advisory Services. He has over 25 years’ experience delivering transformational change and performance improvement in both small and complex organisations, across multiple industries and geographies. Forid draws upon the full range of Arum’s skills and capabilities to support organisations in improving every aspect of their collections and recoveries.
Forid Meah
Head of Advisory