Oboahen1/-Focal-Loss-Engineered-XGBoost-Model-FL-XGBoost-for-Real-Time-Mobile-Money-Fraud-Detection-

Domain:

socioeconomic

Record type:

model

Creator:

Obo

Host:

**FL-XGBoost** replaces standard log-loss with Focal Loss to better detect rare mobile money fraud in Ghana. It offers three novel contributions: a new model variant, a unique Ghanaian dataset, and SHAP explainability aligned with Ghana's Data Protection Act.

Visit

github.com

Comparative Evaluation of Random Forest and XGBoost for Fraud Detection in Mobile Money Payment Systems using SMOTE

Mobile Money Payment Systems (MMPS) have emerged as a rapidly growing tool in the fintech space acro

vnngmalbini/e-xgboost-fl-ghana-dropout

E-XGBoost-FL: Predicting Student Dropout Risk in Northern Ghana # e-xgboost-fl-ghana-dropout E-XGBo

albertameyaw/sp-xgboost-momo-fraud

SHAP-Pruned XGBoost for mobile money fraud detection in Ghana # SP-XGBoost: SHAP-Pruned XGBoost for

Selorm20/mobile-money-fraud-detection

Machine learning project for detecting mobile money fraud in Ghana # ghana-mobile-money-fraud-detec

AshleyNyaboke/mobile-money-fraud-detection

AI fraud detection system for Kenya's mobile money ecosystem — 98% accuracy, built with Isolation F

cdoumb/fraud-detection-mobile-money

Mobile Money fraud detection using EDA and Random Forest — PaySim dataset (Africa) # Mobile Money