financial-fraud

🚨 Fraud Detection with Deep Neural Networks (PoC) 🤖 A hands-on personal project to predict fraudulent financial transactions using deep learning. Covers the full pipeline: from exploratory data analysis (EDA) and preprocessing to model training and evaluation. An experimental approach to tackling real-world financial fraud. 📊🔍

Credit Fraud Detection for the course project for the master's degree in Software and Systems Engineering.

Explainable AI (XAI) based system for detecting financial fraud using machine learning, with model interpretability, analysis, and research-backed implementation.

Developed and evaluated machine learning and deep learning models for detecting financial fraud.

ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.

The Wirecard scandal is considered one of the largest financial scandals of the decade, which caused losses of several billion euros. This analysis examines the digit structure of Wirecard's financial figures in the period from 2005 to 2019 by analyzing the conformity with the expected frequency distributions according to Benford's law. The resu…

End-to-end Explainable Financial Fraud Detection System using XGBoost, SHAP, Hyperparameter Tuning, Threshold Optimization, and Streamlit deployment.

Enterprise-grade fraud & AML detection with ML and deep learning (XGBoost, LightGBM, Autoencoder, LSTM, Transformer). Real-time API, explainability (SHAP), BI export, Streamlit dashboard. PaySim-compatible.

🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯

Mule-ring fraud API — device fan-out + same-amount smurfing on UPI, NetworkX graph + XGBoost SHAP, p50 53ms / 18 rps, hash-chained audit ledger. Reproducible demo: walkthrough video + capture scripts in repo.

A股财务舞弊检测工具 — Beneish M-Score + 多信号引擎

RIDSca helps people check suspicious financial messages, links, documents, media, and APK files before taking action. Built with Streamlit and local open models, it combines scam warning signs, experimental deepfake checks, and practical next steps, with deployment support for Hugging Face Spaces.

Source code of the paper entitled "Spiking Alternatives for the Leaky Integrate-and-Fire Neuron: Applications in Cybersecurity and Financial Threats", and presented at IbPRIA 2025, the 12th Iberian Conference on Pattern Recognition and Image Analysis.

A sophisticated platform for anomaly detection in transaction data using autoencoders. Integrates SQL database connectivity, hyperparameter optimization, entropy analysis, and comprehensive visualizations. Tailored for financial fraud detection and industrial data analytics. (280 characters)

Détection de fraude transactionnelle sur 1,29 million de transactions grâce au Machine Learning, au feature engineering comportemental et au stacking multi-niveaux (CatBoost, XGBoost, LightGBM), avec optimisation des hyperparamètres par Optuna et évaluation par PR-AU

Explainable ML for credit card fraud detection on an imbalanced dataset — imbalance-aware modeling, SHAP, and analyst-ready investigation summaries.

ML model developed using European credit card transaction data to identify suspicious activities.

Explainable and uncertainty-aware financial fraud detection using XGBoost, SHAP, conformal prediction and human-in-the-loop review.

Production-oriented financial fraud detection pipeline with EDA, feature engineering, imbalanced learning, and Random Forest classification.

UPI-oriented mule account detection using behavioral, temporal and transaction-graph learning with intent-aware risk controls.