AI-Powered Autonomous Debt-Management & Collections Strategy
Developed an ML-powered Agentic AI collections strategy for Geldium Finance to predict delinquency risks, improve debt recovery, and enable proactive decision-making. Integrated predictive modeling, SHAP-based explainability, autonomous AI workflows, and responsible AI guardrails to ensure fair and efficient financial risk management.
Overview
Developed an end-to-end Machine Learning and Agentic AI collections strategy for Geldium Finance to modernize delinquency risk management and shift from reactive debt recovery to proactive risk mitigation. Key Contributions & Deliverables: • Predictive Risk Modeling: Engineered credit utilization velocity and payment friction features across credit card portfolio accounts to predict delinquency probabilities. • Explainable AI (XAI): Integrated SHAP reason codes to open model decision logic and meet Fair Lending and Adverse Action notice requirements. • Agentic AI Workflow: Designed a 4-layer autonomous architecture (Data Pipeline, Hybrid Decision Engine, Action Layer, Closed Learning Loop) balancing automated interventions with human-in-the-loop oversight. • Responsible AI Guardrails: Embedded regulatory compliance frameworks (ECOA, FCRA, GDPR, FCA) and Disparate Impact Ratio (AIR) audits to ensure fair treatment across customer cohorts. • Executive Strategy: Synthesized insights into executive reports and slide decks outlining a projected 15% reduction in 30+ day defaults and 40% operational cost savings
Problem
Traditional debt collection strategies rely on reactive approaches, making it difficult to identify customers at risk of delinquency early. This leads to increased defaults, operational costs, and potential fairness and compliance issues.
Solution
Developed an ML-powered Agentic AI strategy to predict delinquency risks, explain credit decisions using SHAP, and automate personalized collection actions with human oversight, improving recovery efficiency while ensuring responsible and fair decision-making.
Features
- 01Predictive delinquency risk scoring
- 02Credit utilization and payment behavior analysis
- 03SHAP-based explainable AI
- 044-layer Agentic AI architecture
- 05Automated collection strategies with human oversight
- 06Responsible AI and fairness monitoring
Technologies
Development details
Developed an end-to-end Machine Learning and Agentic AI solution for Geldium Finance by analyzing credit card portfolio data, engineering predictive features, and building delinquency risk models. Integrated SHAP for model explainability and designed a four-layer autonomous workflow with human oversight, automated interventions, and continuous feedback. Incorporated responsible AI principles to support fair, transparent, and compliant financial decision-making.
Key learnings
- Building predictive models for financial risk assessment
- Applying feature engineering to credit data
- Understanding explainable AI and model transparency
- Designing multi-layer Agentic AI workflows
- Implementing human-in-the-loop decision-making
- Understanding AI fairness, regulatory compliance, and risk management