banking-data

Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.

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Data from the Community Reinvestment Act

Python code to download and process FDIC Summary of Deposits

📊 A comprehensive fraud analytics project analyzing 1 million banking transactions using Python, Exploratory Data Analysis (EDA), data visualization, and statistical testing to identify fraud patterns and financial risk factors.

Our team (Versed Chimpanzee) came first among 340 people and 148 registered teams (119 teams did submission) in TUM Analytics Cup 2022 challenge sponsored by Siemens Advanta Consulting and organized by TUM Informatics Decision Sciences & Systems Department.

A complete mini-project demonstrating how to process, clean, and analyze 100,000 synthetic bank transaction records using PySpark in Databricks. It includes real-world data engineering tasks like data ingestion, null handling, feature engineering, transaction grouping, and business-level reporting, with output stored in Parquet format for BI-ready.

A predictive analysis of banking customer demographics to identify churn risks and optimize retention strategies.

Few cool DS projects

Exploratory Data Analysis on Banking Marketing Data using Python, Pandas, Matplotlib and Seaborn.

BankPrediction is a machine learning project that predicts customer churn, loan defaults, and detects anomalies using XGBoost models. It includes visual data analysis and a future prediction model for 2033.

Customer, card, loan, and transaction analysis of anonymized Czech banking data.

ETL pipelines and SQL maintenance scripts for banking DWH in PostgreSQL

Banking analytics project exploring loan disbursement trends, client activity, and transaction behavior using SQL, Excel, Power BI, and Tableau.

SQL-based fraud detection and risk analysis project using Oracle SQL, focused on identifying high-risk transactions, customer patterns, and fraud chains in banking data

ETL pipelines for loading banking data into PostgreSQL, building DM marts and generating reporting forms

End-to-end banking churn prediction with cross-validation, threshold optimization, SHAP explainability, customer-value scoring and retention prioritization.

Python code to download and parse Community Reinvestment Act disclosures

PPP 대출기관을 감독기관 식별번호(RSSD)에 연결 — 규칙 기반 매칭과 학습 기반 매칭 비교

US bank regulatory data dictionary: MDRM codes, FR Y-9C schedules, reconciliation formulas