A leading European debt collection agency, with operations spanning both amicable and judicial recovery services, supports a large and diverse portfolio of clients across utilities, telecommunications, and financial services. Known for its commitment to regulatory compliance and operational excellence, the agency sought innovative solutions to scale its receivables management.

The Challenge

The organization processes a substantial volume of inbound debtor communications each month. These documents initiate a wide array of collection workflows, ranging from standard payment reminders to complex service inquiries and disputes.

Historically, classification and response routing were managed manually by human agents using a large set of templates. This led to:

• High operational effort and cost

• Inconsistent decision-making due to reliance on tacit knowledge

• A bottleneck in scaling capacity to meet growing demand

The organization aimed to scale operations significantly, targeting multimillion annual case volumes without proportional increases in staff. To support this, a scalable and standardized solution was required.

Solution Deployed

To address these challenges, the organization partnered with Beam AI to introduce a suite of specialized AI agents tailored for specific workflow categories, replacing manual classification with a high-accuracy, transparent AI system.

The first set of AI agents focused on:

1. Insolvency Classification: Automatically detects and processes formal insolvency notifications.

2. Instalment Classification (In development): Validates debtor-submitted proposals for payment plans.

3. Slightly Disputed Case Classification (In discovery): Flags low-severity disputes and maps them to appropriate standardized replies.

4. Service Case Classification (Planned): Routes service-related non-payment inquiries.

Key Features

• Intelligent Prefill in CRM: AI agents automatically populate data fields with suggested classifications and extracted information, leaving final approval to human reviewers.

• Transparent Classification Logic: All AI decisions are rule-based and auditable, eliminating hidden heuristics and reducing bias.

• Continuous Learning Loop: Feedback from human review is fed back into the system, continuously improving accuracy in live environments.

• Agent Factory Framework: A structured development model supports rapid AI agent creation, validation, and deployment, ensuring agility in adapting to evolving workflows.

Implementation Process

The deployment began with deep analysis of historical case data to uncover process patterns and define the scope for initial AI classifications. Beam AI’s system was securely integrated with the client's CRM via a two-way API, allowing seamless interaction between automation and human-in-the-loop workflows.

A dedicated Agent Factory approach was established, enabling the client to consistently design, test, and release new AI agents through defined phases, from discovery and sandbox validation to production rollout.

Preliminary Outcomes

While formal validations are ongoing, initial performance indicators and expectations suggest:

• A marked reduction in average handling time per document

• Substantial improvements in classification consistency

• Scalable infrastructure capable of managing several thousand cases per hour

• All AI decisions reviewed and audited by humans, ensuring compliance and continuous learning

Client Perspective

“The AI-driven system has significantly streamlined our operations. It keeps our human teams in full control while improving the speed, quality, and consistency of our workflows.”

Conclusion and Outlook

The transition to a fully auditable, AI-enabled receivables platform has laid the foundation for a new standard in debt collection management. With further AI agents planned across analytics, exception handling, and new case types, this organization is positioned to lead in operational scalability, compliance, and responsible customer engagement.