banking-analytics

End-to-end Banking Analytics with Dataiku | Data Preparation, Machine Learning, Customer Scoring & Automated Pipelines

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.

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

Oracle SQL-based core banking data analytics project covering customers, accounts, branches, transactions, and banking analytics.

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

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

Machine Learning based Banking Analytics & Risk Prediction System using Streamlit

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.

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

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

Machine learning project for predicting customer term deposit subscriptions

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.

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

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.

A full-stack Banking Management System built with Flask and MySQL, integrated with Machine Learning for real-time transaction fraud detection, risk scoring, complete CRUD management (Customers, Accounts, Loans, Cards, Employees), interactive analytics, and automated PDF/Excel reports.