The Challenge
A regional consumer electronics recommerce company operating a device financing business model faced increasing difficulty managing credit risk and fraud at scale.
The organization relied on a generic third-party credit score for underwriting decisions. While initially sufficient, the approach became problematic as the business matured and customer volumes increased.
Within 1–2 years of operation:
- Non-Performing Loans (NPL) rose to double-digit levels
- Management confidence in underwriting decisions declined
- Fraud-related losses were increasingly misclassified as pure credit risk
The generic scoring model lacked alignment with the company's unique customer segments, transaction behavior, and device-financing risk profile, and offered limited explainability for audit and risk review.
Why the Problem Was Critical
- Rising enterprise risk management exposure from undetected fraud
- Over-reliance on black-box models with limited transparency
- Increasing regulatory and audit scrutiny on credit decisioning
- Inefficient manual reviews and delayed underwriting decisions
The company needed a solution that could:
- Separate fraud risk from credit risk
- Improve data interpretation across fragmented data sources
- Deliver explainable, auditable AI decisions
- Scale securely within a regulated environment
Why AiMod Was Selected
The organization selected AiMod due to its ability to deliver bespoke, explainable decision intelligence, rather than generic scoring. Key differentiators included:
- Support for agentic AI and AI agents working in an agent-to-agent solution
- End-to-end coverage across data management, data integration, model development, deployment, and monitoring
- Native ability to embed business logic and underwriting policies directly into models
- Governance-ready architecture with strong AI security and data sovereignty
Solution Overview
Using Infomina AiMod, the company implemented a custom credit risk and fraud decision intelligence platform.
AiMod Capabilities Used
- AI automation for data mining, data preparation, and data quality management
- Semantic models and modular pipelines for consistent analytics workflows
- Business process management for underwriting and fraud decisioning
- Enterprise database management system integration across historical and operational data
AI-to-the-Data Execution
AiMod was deployed using an AI-to-the-Data architecture:
- Historical credit, repayment, and operational data remained within enterprise systems
- Secure data integration pipelines powered analytics without data centralization
- Business rules, underwriting policies, and risk appetite were explicitly encoded into the models
This ensured data sovereignty, auditability, and regulatory readiness.
Results & Impact
Operational Improvements
- Automated credit underwriting decisions
- Real-time fraud identification during approval
- Improved portfolio monitoring and risk segmentation
- Faster iteration of business rules and model logic
Decision Quality
- Clear separation between fraud risk and credit risk
- More accurate approvals and rejections aligned with actual risk
- Higher trust in data analytics, business analytics, and decision outputs
Measurable Outcomes
- Total NPL reduced from double-digit levels to below 2% within 12 months
- Immediate operational improvements post-deployment
- Portfolio performance improvements observed within months
Governance, Explainability & Compliance
AiMod delivered:
- Fully governed and explainable AI
- Transparent linkage between model logic, decisions, and outcomes
- Clear audit trails from data ingestion to final decision
- Support for model performance validation, including statistical measures such as the coefficient of variation
Both business and risk teams could clearly understand and defend every decision.
