MSc FinTech — National College of Ireland Specialising in fraud detection, credit risk analytics, and regulatory compliance.
- 🎓 MSc in Financial Technology — National College of Ireland (2024–2026)
- 🔬 Research: AI-driven fraud detection, credit risk modelling, EU AI Act compliance & human oversight
- 🛡️ Domain focus: Financial crime prevention, AML/KYC analytics, prudential credit risk, regulatory data analysis
- 📊 Projects span real datasets — Bank Account Fraud (NeurIPS), ULB Credit Card, META options chain
- 📫 fahadlatheef005@gmail.com
Languages & Analysis
ML & Modelling
Compliance & Risk Frameworks
End-to-end fraud detection pipeline (FFNN + XGBoost, ROC-AUC 0.876) on the Bank Account Fraud dataset. Pre-registered behavioural study (n=35) testing whether counterfactual explanations reduce automation bias — directly addressing EU AI Act Article 14 human oversight requirements.
Key finding: 71% of decisions made in under 5 seconds — explanation display alone does not satisfy Article 14 intent.
Python XGBoost TensorFlow SHAP DiCE Streamlit Regulatory Compliance
Production-style fraud detection on the ULB Credit Card dataset (284,807 transactions, 0.17% fraud prevalence). Covers SMOTE class balancing, XGBoost, LightGBM, precision-recall threshold tuning, and SHAP explainability.
Python XGBoost LightGBM SMOTE SHAP AML Analytics
Section 1: EDA of a global crypto enforcement fines database — jurisdictions, violation types, temporal trends (2009–2024). Section 2: Credit risk classification (Logistic Regression, Random Forest, XGBoost, DNN) with class imbalance handling.
Python Credit Risk Regulatory Data Decision Tree DNN FinCrime EDA
"Compliance without understanding is just box-ticking. The goal is meaningful oversight."