banking-analytics

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

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

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.

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

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.

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

Machine learning project for predicting customer term deposit subscriptions

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

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

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.

📊 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.

Banking-focused customer segmentation using K-Means, Hierarchical Clustering, and DBSCAN on the South German Credit dataset, with post-hoc credit-risk analysis.

📊 Banking Analytics Dashboard built with Power BI — exploring customer demographics, financial health, transaction behavior & card insights across 4 analytical pages with DAX-powered KPIs.

Explainable AI-powered credit risk scoring system with loan approval workflows, fairness monitoring, SHAP explainability, and interactive Streamlit dashboards for responsible financial risk analytics.

EDA project analyzing customer behavior in bank marketing campaigns

Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

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.