Simulação de concessão de crédito em uma instituição financeira. Analisa variáveis como renda, idade e histórico de inadimplência para entender padrões de aprovação e reprovação, gerando insights estratégicos para decisões baseadas em dados.

Coding assignments of the "Machine Learning in Finance & Insurance" course at ETH Zürich (Fall 2024).

Predict financial risk using behavioral and demographic data from the 2021 FinAccess Household Survey (KNBS). Built with Streamlit and XGBoost.

Production-ready FastAPI service that converts Credit Reports (PDF format) into structured JSON data using CreditGraph AI patterns with automatic PII scrubbing for data privacy.

The Credit Product Recommendation Engine

End-to-end AI Fraud Detection & Transaction Monitoring project using SQL, Python, ML models, SHAP explainability, and FastAPI integration.

A dirty-work toolbox for data analyses about fixed income securities, developed by only me (not the institute) as an intern data analyst.

A predictive credit scoring system using alternative behavioral and demographic data from the 2021 FinAccess Survey to assess household loan default risk in Kenya.

In diesem Projekt entwickle ich eine vereinfachte „Mini-Schufa“ mithilfe des Machine-Learning-Modells Random Forest. Ziel ist es, die Kreditwürdigkeit eines Nutzers anhand seiner Eingaben einzuschätzen

Data preparation, predictive modeling and classification, conclusions and recommendations. Preparation and modeling preformed in Python. Work in progress.

Deep learning-based credit risk prediction system using neural networks to assess loan default probability. Implements multiple ML/DL models with feature engineering and class imbalance handling. Built with PyTorch for accurate credit scoring and risk assessment.

Unlimited credit works

DEBT DESTROYER - Avalanche Method Tracker

This project potray's my capstone work. My work involved cleaning, manipulation and analyzing huge data sets and implementing ML models for predictive analysis. This analysis demonstrates that data-driven credit interventions can meaningfully reduce financial exposure while balancing customer retention.

AI-Powered Alternative Credit Scoring for 2+ Billion Unbanked People Worldwide

A simple, interpretable credit approval model built on HMDA data using decision trees

This project analyzes credit card customer & transaction data to uncover key business insights.

This project analyzes credit card transaction and customer data to uncover revenue trends, spending patterns, and customer demographics. Using SQL Server for data storage & transformation and Power BI for visualization, the dashboard delivers real-time insights into key performance metrics

📊 Analyze credit data to uncover insights that boost customer retention, revenue growth, and effective risk management for financial institutions.

Plim AI First