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AI-Driven Alternative Credit Scoring

Phase 1 — Regulatory Mapping & Data Governance Setup

 

Objective: Establish a legally compliant, secure, and ethically governed data environment.
Key Activities:

  • Assess regulatory boundaries with central banks and data-protection authorities.

  • Map all data sources (utility bills, QR-payment history, telco data, transactional behavior, employment patterns).

  • Develop government-endorsed data-privacy protocols and consent frameworks.

  • Define credit model risk guidelines and compliance with AI/ML fairness standards.

  • Set up a secure data lake and encrypted storage systems.

Phase 2 — Data Acquisition & Multi-Source Integration

Objective: Gather alternative datasets that reflect real financial behavior beyond traditional credit files.
Key Activities:

  • Integration with QR payment ecosystems for behavioral spending patterns.

  • Partner with utilities, telcos, and payroll systems for real-time data feeds.

  • Government integrations for public records (where legally allowed).

  • Ensure standardized formats and API-based ingestion.

  • Data cleansing, normalization, and outlier detection.

Phase 3 — AI Model Development & Scoring Framework Design

 

Objective: Build a transparent, explainable, and bias-mitigated AI scoring model.
Key Activities:

  • Feature engineering from alternative data sources (payment reliability, income stability, merchant interactions).

  • Train ML models (Gradient Boosting, Neural Networks, GraphAI for behavior patterns).

  • Perform fairness checks to avoid demographic or socioeconomic bias.

  • Create explainability layer (XAI) required by governments and financial institutions.

  • Develop multiple risk tiers: Nano, Micro, MSME, and Informal-Sector Scoring.

Phase 4 — System Infrastructure & API Deployment

 

Objective: Deploy a scalable infrastructure to generate scores in real time.
Key Activities:

  • Build secure scoring APIs for banks, lenders, fintechs, and government agencies.

  • Implement risk-engine rules for credit limits, repayment patterns, and alerts.

  • Integrate real-time monitoring dashboards for underwriters and regulators.

  • Develop merchant and MSME credit profiles.

  • Test for extreme loads and transaction bursts (finance-grade stress testing).

Phase 5 — Pilot Testing & Field Validation

 

Objective: Test the scoring model on targeted demographics to validate accuracy and inclusivity.
Key Activities:

  • Pilot with micro-lenders, government MSME programs, and community banks.

  • Compare predicted risk vs. actual repayment performance.

  • User testing with informal workers, delivery riders, market vendors, etc.

  • Calibration of score boundaries and risk coefficients.

  • Fine-tuning based on ground-truth feedback and regulator observations.

Phase 6 — Full Rollout & Market Adoption

 

Objective: Launch the scoring system across multiple financial ecosystems.
Key Activities:

  • Provide scoring services to banks, fintechs, BNPL platforms, and government funds.

  • Enable instant credit lines for QR users based on behavioral trust scores.

  • Onboard merchants to build MSME credit visibility.

  • Educate the public about alternative scoring to increase trust.

  • Coordinate with government support programs (MSME loans, social credits, youth financing).

Phase 7 — Continuous Monitoring, Governance & AI Evolution

 

Objective: Maintain scoring accuracy while expanding capabilities.
Key Activities:

  • Continuous model retraining with new datasets.

  • Bias audits, compliance checks, and AI ethics reporting.

  • Expand to cross-border scoring for migrant workers and expats.

  • Introduce advanced layers: prediction of financial stress, spending patterns, and micro-loan eligibility.

  • Quarterly reviews with regulators and partner institutions.

Phase 8 — National Integration & Future Enhancements

 

Objective: Position the scoring system as a national digital infrastructure for financial inclusion.
Key Activities:

  • Integrate scoring into national ID systems or digital wallets (if approved).

  • Use scores to unlock government-backed MSME lending programs.

  • Deploy AI scoring for public services such as rent guarantees or utility deposits.

  • Explore CBDC scoring alignment (transactional stability indicators).

  • Regional expansion across GCC, MENA, and South Asia through government MOUs.

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