Data-driven Credit Risk Analysis using MySQL (rule-based & explainable)
-
Updated
Mar 8, 2026
Data-driven Credit Risk Analysis using MySQL (rule-based & explainable)
"ML pipeline for customer churn prediction and risk segmentation using Python"
SQL- und Python-basierte Analyse von Kreditrisiken mit Fokus auf Risikosegmentierung, Ausfallwahrscheinlichkeit und datengetriebene Handlungsempfehlungen.
End-to-end Credit Risk Analytics project featuring PD modeling, risk segmentation, portfolio monitoring, SQL risk marts, and interactive Power BI dashboards.
An End-to-end financial analytics project using SQL & Python to monitor loan performance, identify high-risk loans, and generate decision-ready insights. Demonstrates portfolio analysis, KPI development, and BI reporting aligned with industry-standard data analyst practices.
A regulated fintech platform enabling lending, remittance, and credit access across banked and unbanked segments. Integrates e-money issuance, Stellar-based cross-border remittances, prepaid cards, and hybrid credit scoring (CMAP + behavioral data), with embedded lending partnerships.
People analytics pipeline analysing 1,470 IBM employees, identifying Sales Rep attrition at 39.8% and segmenting 264 high risk active employees by composite flight risk score.
Customer Churn Analysis (SaaS Domain)
Add a description, image, and links to the risk-segmentation topic page so that developers can more easily learn about it.
To associate your repository with the risk-segmentation topic, visit your repo's landing page and select "manage topics."