Fraud Detection on credit card transations

Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.

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.

Real-time Credit card Fraud detection using Spark Streaming, Spark ML, Spark SQL, Kafka, Cassandra and Airflow

Agentic workflow with SQL, RAG (vector DB + web search), and transaction fraud detection agents.

This project focuses on detecting fraudulent transactions in a bank using structured query language (SQL). Using a transactional dataset, we perform comprehensive exploratory data analysis (EDA), apply fraud detection logic, and generate actionable insights.

A SQL-based fraud detection project using PostgreSQL to analyze transaction data for a bank and uncover high-risk behaviors, repeat offenders, and location-based anomalies.

Fraud Detection in E-Commerce (SQL): Normalisation with Python, analysis with PostgreSQL to research fraud patterns.

My solution to fraud detection task using ClickHouse SQL

Analyzing cyber insurance claims with SQL to uncover fraud, financial anomalies, and industry trends. Key insights include loss discrepancies, evolving cyber threats, and payout patterns.

End-to-end fraud detection analytics project using Python, SQL Server, and Power BI

SQL Data Warehouse project for banking transactions using Bronze–Silver–Gold architecture. Includes data cleaning, schema design, and KPI analysis for fraud detection and customer segmentation

SQL-based project focusing on operational metrics, customer segmentation, and fraud detection. Key areas: Serial returners analysis (high-risk customers), net sales calculation, delivery partner performance, and identifying top return reasons per company to improve quality.

A SQL-based backend system for managing digital gift cards, built with MySQL. Includes full support for card creation, redemption, recharges, transfers, fraud detection, reporting views, and trigger-based validations. Designed for robust financial transaction logging and real-world database practices.

Objective: Create system to identify/detect Credit Card Fraud, Ingest data using Apache Sqoop , process the streaming data and make Real-time decisions Solution: Load data in No Sql , Ingest data using AWS RDS ,Create look up tables , Created streaming framework for real time data Key Achievement: Loaded Data and Created Look up tables , Created a streaming data processing framework that ingests real-time POS transaction data from Kafka, Validation system from Fraud detection

hadoop-projects IBM stock project Get IBM stock dataset Clean the dataset Load dataset on the HDFS Build MapReduce program Process/ Analyse result Hadoop set up Run single node Hadoop cluster /usr/local/Celler/hadoop Check : https://www.slideshare.net/SunilkumarMohanty3/install-apache-hadoop-on-mac-os-sierra-76275019 http://zhongyaonan.com/hadoop-tutorial/setting-up-hadoop-2-6-on-mac-osx-yosemite.html Go to :http://localhost:50070/dfshealth.html#tab-overview Start : hstart Hadoop command: hadoop fs -ls hadoop fs -mkdir /hbp Upload a file in HDFS hadoop fs -put <localsrc> ... <HDFS_dest_Path> go to : http://localhost:50070/explorer.html#/hbp/ibm-stock Dataset head date - opening stock quote - high - low - traded volume - closing price Clean dataset with command : awk,sed,grep Run the program Copy jar to Hadoop Run the program on Hadoop system: hadoop jar /hbp/ibm-stock/ibm-stock-1.0-SNAPSHOT.jar /hbp/ibm-stock/ibm-stock.csv /hbp/ibm-stock/output Check output dir : hadoop fs -ls /hbp/ibm-stock/output Copy file from HDFS to local file system : hadoop fs -get /hpb/ibm-stock/output/part-r-00000 home/Users/hien/results.csv Check head home/Users/hien/results.csv Customer Analysis Collect data Customer master data : MySQL Logs : text file Twitter feeds : JSON Load data from data sources in HDFS Mug data Create table in Hive to store data in format Query and join tables Export data Set up stack: Hortonwork data platform HDP Install HDP sandbox: HDP 2.3 HDP : hive, squoop , Fraud Detection system Clean dataset Create model Using: Spark and Hadoop Problem: predict payment transaction is suspect Build model : Find relevant field: Apache Spark 2 Spark ecosystem : Spark core Spark streaming Spark SQL MLlib GraphX Spark-R Apache Spark component: + navigate to : localhost:4040 run spark-shell : $SPARK_HOME/bin/spark-shell Word count Create pairRDD : valpairRDD=stringRdd.map( s => (s,1)) Run reducebykey to count the occurency of each word : alwordCountRDD=pairRDD.reduceByKey((x,y) =>x+y) Run the collect to see the result : valwordCountList=wordCountRDD.collect Find the sum of integers Create RDD of even number from integers : valintRDD = sc.parallelize(Array(1,4,5,6,7,10,15)) Filter even numbers from RDD : valevenNumbersRDD=intRDD.filter(i => (i%2==0)) Sum the even numbers from RDD : val sum =evenNumbersRDD.sum Count the number of words in file : Read txt file : cat people.txt Read file from Apache Spark shell : val file=sc.textFile("/usr/local/spark/examples/src/main/resources/people.txt") Flaten the file, prcess and split , with each word : valflattenFile = file.flatMap(s =>s.split(", ")) Check the content of RDD : flattenFile.collect Count all words from RDD : val count = flattenFile.count Working with Data and Storage + Chua hoc 4 (RDD transformation),

SQL Fraud Detection

SQL Fraud detection assignment.

SQL queries for fraud detection

RPA Fraud Detection Using SQL

No description available

This project moves beyond simple anomaly detection. I have attempted to engineer a production-grade data pipeline that uses Recursive SQL to traverse network graphs and identify high-risk clusters that traditional "flat" queries miss.

A comprehensive fraud detection system that analyzes credit card transactions using SQL, PostgreSQL, Python, and data visualization techniques.

No description available

A simple and interactive dashboard built using MySQL, Python and Streamlit to identify suspicious credit card transactions. The system analyzes micro-transactions, early morning patterns, and merchant activity to highlight potential fraud indicators using real-time SQL queries and visualizations.

It is a data base Using SQL SERVER

Credit Card Fraud Detection SQL Project

Fraud Detection using Python and SQL

A project showcasing a financial fraud detection system using MySQL with features such as risk scoring, anomaly detection, and auditing.”)

No description available