Artificial intelligence has become an integral part of our everyday lives. Increasingly, people are turning to ChatGPT instead of traditional search engines, asking AI for weekend trip plans or jacket recommendations for a mountain hike. But the real revolution is still on the horizon—especially in the financial sector and receivables management. With the AI Act coming into effect, companies must not only recognize the potential of these new technologies but also develop strategies for their safe and legally compliant implementation.
Generative AI, along with language models and machine learning systems, is unlocking unprecedented opportunities for process optimization and increased effectiveness in debt management. However, using these tools brings both immense potential and specific risks. When implementing AI within an organization, one must be aware of the requirements set out by the AI Act, which came into force on February 2, 2025. The regulation defines the principles for the development, implementation, and use of artificial intelligence within the European Union.
AI system providers introducing products to the EU market are required to ensure their solutions meet the AI Act standards. This includes product labeling, providing detailed system information, and adhering to transparency and accountability principles. AI users must implement risk management procedures for these systems, and establish rules for data governance—with a strong emphasis on protecting personal, financial, and processing security data within the EU.
Practical Applications of AI in Debt Collection
Receivables management offers numerous opportunities for AI adoption. Key areas include:
- Personalized Debtor Communication: AI algorithms can tailor the tone and repayment offers to an individual debtor’s profile—considering their payment history, communication preferences, and other factors. These systems continuously learn and adapt strategies in real time for maximum effectiveness.
- Chatbots in Debt Collection: AI-powered chatbots are now commonly used to interact with debtors. These bots conduct personalized conversations, remind customers of upcoming payments, and negotiate repayment terms. Their 24/7 availability allows engagement at convenient times without involving human staff. Advanced chatbots also detect sentiment and adjust communication tone accordingly.
- Process Mapping: AI can analyze the entire debt collection lifecycle—from the moment a receivable arises until it’s paid—highlighting bottlenecks and inefficiencies. This data-driven approach allows process optimization based on facts rather than managerial intuition.
- Document Analysis: Legal departments benefit from AI-driven document processing. Using OCR technology, AI can read and convert texts from various formats, categorize legal documents, and extract key data. This has been implemented in the extended functionality of our VSoft Court Portal Connector, integrated with the Polish Common Courts Information Portal. Powered by Azure Document Intelligence and ChatGPT, the system can identify judgment types, party details, awarded amounts, interest, and due dates—handling diverse formats and inconsistent court document structures. This automation dramatically reduces manual review time and error risk.
- Inefficiency Detection: By analyzing historical data, AI identifies process stages with the most delays or errors, allowing targeted optimization. For example, it may spot failed contact attempts with debtors at certain times and recommend better scheduling.
- Strategy Optimization: AI can predict the most effective contact method (email, SMS, phone) and negotiation tactics for specific customer segments by analyzing historical performance.
- Case Risk Classification: Automatic classification by debt recovery likelihood helps prioritize cases—letting teams focus on high-risk accounts while automating simpler ones.
- Public Register Monitoring: AI can monitor public registers (e.g., KRS, BIG, CEIDG) for early signs of insolvency risk among business partners, enabling preventive action before a financial collapse.
Risks of Implementing AI
Deploying AI systems comes with notable risks. Recent examples underscore the importance of risk awareness:
In 2024, an Air Canada passenger won a lawsuit after an AI chatbot incorrectly informed him of a refundable ticket policy. When the airline refused reimbursement citing their fare policy, the court ruled they were liable for the chatbot’s misinformation. This precedent shows the critical need for accuracy in AI-generated content.
Another case involved a GM car dealership whose chatbot was tricked by a customer into offering a $76,000 luxury vehicle for $1. Though the deal never closed, it exposed how easily users can exploit AI models.
These incidents demonstrate the necessity of a comprehensive risk management strategy encompassing technology, compliance, and organizational structure.
AI Implementation Strategy in Debt Collection
Successful AI deployment requires a thoughtful approach, including:
- Defining Business Goals: Identify the specific challenges AI should solve—automating customer service, improving recovery rates, fraud detection, or court process optimization. Evaluate both off-the-shelf solutions and custom systems.
- Assessing Organizational Readiness: Review IT infrastructure and employee skills. Without adequate foundations, AI projects can become costly and ineffective. A readiness audit can highlight gaps to address.
- Choosing Technologies and Tools: Decide between third-party solutions (quick but less flexible) or building proprietary systems (customizable but resource-intensive).
- System Integration: Seamless integration with banking, CRM, accounting, and risk systems is critical. Poor integration can cause operational errors and data fragmentation.
- Regulatory Compliance: In finance, regulatory alignment is crucial. AI must comply with GDPR, PSD2, local financial authorities (e.g., KNF/EBA), and now the AI Act. Legal audits and data protection measures (e.g., anonymization, tokenization) are mandatory.
- Building an AI Team: Create an interdisciplinary team to lead AI adoption. Collaboration with business units ensures alignment with real organizational needs. The team should be lean but highly motivated to overcome internal resistance.
- Testing and Piloting: Start small. Controlled tests with representative training data can validate models and reduce errors before full-scale rollout.
- Monitoring and Optimization: Continuous performance tracking and model updates are essential. Implement safeguards against manipulation and ensure constant system improvement.
- Employee Training and Culture Shift: Educate staff on AI capabilities and workflows. Include training, workshops, and AI-human collaboration protocols as part of a broader cultural shift.
- Scaling AI Organization-Wide: After a successful pilot, expand AI use into other areas—fraud detection, scoring, legal recovery. Gradual scaling helps manage risk and maximize ROI.
Conclusion
Artificial intelligence offers immense value in debt collection, but recognizing its limitations and managing risk is essential. Models must be trained on high-quality data and protected from internal and external threats. Data security and model integrity should remain top priorities.
Implementing AI in financial institutions is a complex process requiring clear objectives, IT integration, legal compliance, and skilled teams. Continuous testing, monitoring, and education are critical to success.
Organizations that adopt AI responsibly and strategically will gain a competitive edge through process optimization, cost reduction, and improved recovery rates. In the era of digital transformation and under the AI Act, using AI is no longer optional—it’s a necessity for staying competitive.
This article appeared in Puls Biznesu: https://www.pb.pl/ai-w-windykacji-jak-wykorzystac-potencjal-sztucznej-inteligencji-w-procesie-odzyskiwania-naleznosci-1237166
