banking-analytics

End-to-End Credit Risk Analytics Dashboard using Power BI and Python (EDA, Correlation, Risk Modeling, Network Analysis)

Fortune-500-grade banking analytics platform: OLTP -> medallion lakehouse -> Kimball star schema -> semantic layer -> 9-tab executive dashboard + 5 ML models (churn, fraud, segmentation, forecasting). Production-ready, governed, fully tested.

Enterprise-style Credit Risk Analytics & Scorecard Modeling System using WOE, IV, Logistic Regression, XGBoost, KS, AUC, Credit Scoring, PSI & Drift Monitoring.

This project analyzes 284,000+ banking transactions to detect suspicious activity using time-series anomaly detection and an Agentic AI investigation workflow.

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.

Machine learning project for predicting customer term deposit subscriptions

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.

SQL and Tableau project analyzing credit card customer segmentation, revenue concentration, and spending behavior.

End-to-end bank customer churn prediction — EDA, feature engineering, Random Forest & Gradient Boosting models, interactive Streamlit app. Built with Python, Scikit-learn & Plotly.

Business-oriented SQL and Power BI project analyzing customer behavior, deposits, loan performance, digital banking adoption, and risk analytics.

Executive banking intelligence dashboard for analyzing customer conversions, campaign performance, and banking KPIs using Power BI, SQL, Python, and DAX.

End-to-end banking campaign analytics project using Power BI, SQL, Python, and statistical analysis to uncover customer behavior, campaign performance, engagement patterns, risk insights, and macroeconomic impact on subscription conversion.

📊 Analyzed bank customer churn data using Python and Power BI to uncover key factors influencing customer attrition and deliver actionable business insights through an interactive dashboard.

Capstone project: employee engagement vs customer satisfaction vs branch performance (R, regression, clustering, Shiny)

Exploratory analysis of 3,000 retail bank customers — demographics, account balances, and risk profile.

EDA project analyzing customer behavior in bank marketing campaigns

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.

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

An end-to-end banking analytics system leveraging SQL/RDBMS and Power BI. Features a normalized database and interactive dashboards for Customer 360, segmentation, loan risk, branch performance, and product analytics to drive strategic, data-driven decisions.

Research/educational multi-report Power BI + Microsoft Fabric (PBIP) monorepo — agent-built with Claude Code. Includes a banking 'Branch & Channel Performance' report on 100% dummy AI-generated data. Not production-tier.