AI is transforming credit collections by predicting borrower behavior, segmenting delinquency risk, and personalizing outreach strategies. This training introduces predictive analytics, NLP, and reinforcement learning techniques to optimize recovery rates and reduce churn. Participants will explore real-time scoring, emotion-driven responses, and ethical automation of collections using advanced AI models.
- Behavioral modeling for repayment likelihood and delinquency
- Segmentation and targeting using AI-driven risk profiles
- Predictive triggers for automated outreach timing
- NLP for interpreting borrower communication sentiment
- Emotion AI for empathetic messaging and escalation handling
- Reinforcement learning for optimized collection policies
- Integration of ML models with CRM and collection systems
- Ethical considerations and bias mitigation in collections AI
- Performance dashboards and KPI tracking with AI support
- Regulatory compliance and explainability of AI actions
- Predict borrower behavior and risk of default using ML models
- Design dynamic segmentation for tailored collection strategies
- Apply NLP to analyze borrower responses and emotional tone
- Deploy reinforcement learning for decision policy optimization
- Ensure ethical and regulatory-aligned collections automation
- Integrate AI insights into core CRM and collection workflows
- Measure impact of AI tools on key collection KPIs
- Create explainable models for internal and external audits
- Credit collections managers and specialists
- Data scientists in finance and customer recovery teams
- CRM integration and AI engineering teams
- Compliance and risk governance professionals
- Consumer experience and analytics experts
- Loan operations and strategy officers
- Instructor-led sessions with live AI demos
- Hands-on labs using real-world borrower data (anonymized)
- NLP and ML model building in guided exercises
- Reinforcement learning simulation for collections decisions
- Workshops for ethical frameworks and compliance alignment
- AI-in-the-loop decision architecture mapping
- Collaborative case study development and group challenges
- Credit collections lifecycle and AI transformation points
- Model types: behavior prediction, classification, ranking
- Lab: build a binary classifier for repayment probability
- Discussion: ethical dimensions of collections automation
- NLP for interpreting borrower tone and intent
- AI-driven segmentation for strategy customization
- Lab: sentiment analysis on borrower text responses
- Tool walk-through: using clustering for collections design
- Fundamentals of RL in decision optimization
- Q-learning and policy value tradeoffs in collections
- Simulation: train an RL agent for collection call strategy
- Evaluation: ROI and ethical balance of model actions
- Embedding ML/NLP models into CRM systems
- Workflow automation and real-time decision engines
- Lab: build a microservice pipeline for collections scoring
- Case study: enterprise deployment of AI in collections
- Bias mitigation and fairness in collection modeling
- Monitoring KPIs and feedback loops
- Audit-ready explainability and traceability standards
- Workshop: design a compliant AI-enabled collections roadmap
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.