Proyek ML untuk segmentasi nasabah bank menggunakan K-Means Clustering dan prediksi segmen dengan model Klasifikasi. Fokus pada analisis perilaku untuk mendukung keputusan bisnis.

End-to-end analysis of bank loan default risk using historical lending data to identify key risk factors, assess borrower behavior, and support data-driven credit decisions.

An end-to-end ML application that predicts bank customer churn using 9 different models and provides AI-generated retention strategies with Groq LLM. Built with Streamlit for interactive predictions and visualizations.

📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.

Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

End-to-end Canadian Credit Risk & PD modeling project using public Canadian lending data, ML models, SHAP explainability, Streamlit UI, and Power BI dashboard.

Predict loan approvals using machine learning with SHAP explainability. Analyze customer data, build interpretable models, and visualize feature impact for business decision support.

Customer churn prediction project using EDA, feature engineering, SMOTE balancing, and machine learning models (Random Forest & XGBoost). Includes model evaluation, business insights, and retention strategy recommendations for banking analytics

Analyzed bank loan application and repayment data using sql and power bi to evaluate approval trends, risk factors, and loan performance.

Loan Default Analysis - Multi-file joins, DateTime operations, String handling, DTI calculations

End-to-end bank loan performance analysis using SQL and Power BI, focusing on loan distribution, repayment trends, risk analysis, and key financial KPIs through interactive dashboards

Machine learning–driven loan default risk prediction dashboard using XGBoost with transparent, case-specific credit risk explanations.

🔍 Sistema de alerta temprana de Churn para Andes Bank. Análisis de causalidad mediante Python para identificar la fricción operativa como driver principal de abandono (99.5% de riesgo ante quejas). Incluye ETL, EDA bivariado y recomendaciones estratégicas de retención.

Retail banking analysis covering customers, accounts, transactions, loans, cards, and service feedback using SQL, Python, and Power BI.

Time series modelling and FTE planning based on loan application data from a Big 4 Australian bank

This repository showcases a proof of concept of my work at Bartronics India Ltd containing Power BI dashboards and a custom SQL stored procedure developed for monitoring Banking Correspondent (BC) performance, transaction trends and rural banking KPIs in the Financial Inclusion System.

This project explores customer behavior using the Bank Marketing dataset to predict term deposit subscriptions. It includes EDA, feature engineering, model training, class imbalance handling, and evaluation using a logistic regression model.

EDA and visualization of banking loan applicant data to assess credit risk and support data-driven lending decisions.

End-to-end Data Warehousing and Business Intelligence solution for banking operations. Features comprehensive ETL pipelines using SSIS, Star Schema modeling in SQL Server, and OLAP Cube creation with SSAS.

Rules based KYC Risk Scoring Dashboard -SQL and PowerBI. Automates customer classification into Low/Medium/High risk tiers using onboarding data.