Predictive AI for Collections & Credit Risk Optimization
Varunbabu Tumeti
Dec 01 2025|10 min read
Problem Statement
- One-size-fits-all collections approach produced low recoveries and wasted agent effort.
- Early risk indicators were missed, increasing late payments and defaults.
- Manual processes drove high operational costs and inconsistent customer handling.
- Existing models lack product-level granularity and real-time adaptability, preventing timely, tailored interventions.
Project Objectives
- Implement AI-driven borrower segmentation and risk scoring for prioritized outreach.
- Forecast DPD trajectories to enable proactive interventions.
- Reduce Collection team’s manual workload and reallocate agents to high-impact accounts.
- Increase recovery rates across risk tiers with segment-specific strategies.
- Operationalize real-time scoring and feedback loops for continuous adaptation.
Scope of Work
- Consolidate and enrich data (transactions, payments, product, call outcomes) on Databricks.
- Build forecasting models to predict DPD logs and short-term delinquency.
- Implement probabilistic segment mapping and behavioral clustering for targeted treatments.
- Deploy scoring pipelines to integrate scores into CRM and collections workflow.
- Create dashboards for performance monitoring and business adoption.
- Establish model governance, validation, and audit trails.
KPI Snapshot
| Metric | KPI uplift |
|---|---|
| Borrower Risk Prediction Accuracy | 85% |
| Collections Workload (manual touches) | 20–30% reduction |
| Recovery Rate (High-Risk) | +60% |
| Recovery Rate (Low-Risk) | +10% |
| Average Days to Resolution | Reduced by 25% |
Timeline and Delivery Phases
- Discovery & Data Prep
- 4 weeks: data ingestion, quality checks, feature design.
- Model Development & Validation
- 8 weeks: model training, back testing, threshold tuning.
- Production & Monitoring
- 8 weeks: deploy to Azure ML, integrate with CRM, set up dashboards and alerts.
Approach Followed
- Data engineering on Databricks to unify signals and create time-series features.
- Probabilistic segment mapping to convert DPD behaviors into dynamic risk segments.
- LightGBM and XGBoost models for DPD forecasting and near-term default risk.
- Behavioral clustering to define treatment buckets and script personalized outreach.
- Real-time scoring pipeline in Azure ML with continuous feedback loops to refresh segments.
- Monitoring and retraining workflows using MLflow and Azure Monitor; visualized KPIs in Power BI.
Integration and Operationalization
- Scores exposed via REST APIs and streamed into the CRM and collections engine for automated prioritization.
- Orchestration with existing campaign tools to trigger channel-specific actions (IVR, SMS, email, agent queues).
- Role-based dashboards for Collection team managers, risk teams, and compliance officers.
Governance, Compliance, and Risk Controls
- Model validation framework with holdout back tests, PSI monitoring, and fairness checks.
- Thresholds and action rules documented and versioned; approval gates before production rollout.
- Audit logging of scores, decisions, and human overrides for regulatory traceability.
- Performance SLAs and automated alerts for drift, latency, and data quality issues.
Impact Delivered
- 85% borrower risk prediction accuracy.
- 20–30% reduction in collections workload through prioritized outreach.
- 60% uplift in recovery rates for high-risk borrowers; 10% uplift for low-risk segments.
- Faster resolution times, improved agent productivity, and lower cost-to-recover.
Technology Stack
- Languages: Python
- Frameworks: LightGBM; XGBoost
- Platforms: Databricks; Azure ML Services
- Tools: MLflow; Azure Monitor; Power BI; REST APIs for score delivery
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