This is about using smart algorithms to decide who should get a loan, how much, and at what interest rate—by looking at far more data than a human could and doing it in seconds instead of days.
Traditional lending and credit scoring are slow, manual, and often rely on limited or outdated data, leading to higher risk, exclusion of creditworthy borrowers, and higher operational costs for lenders.
Access to proprietary borrower data, historical repayment behavior, and tightly integrated workflows with core banking/lending systems, plus regulatory know‑how around explainable AI models.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Model governance, explainability, and regulatory compliance as portfolio size and model complexity grow.
Early Majority
Focus on AI-driven credit risk modeling and loan decisioning across the full lending lifecycle, emphasizing automation, alternative data usage, and financial inclusion rather than just traditional bureau-score-based underwriting.