Bank-style Credit Risk Scorecard using Logistic Regression, IFRS-9 Expected Credit Loss, and an Interactive Streamlit Risk Dashboard for loan default prediction.

Projeto da API do primeiro semestre de 2026

A Python library providing elegant, type-safe primitives for building the kinds credit models that typically force teams to reach for Excel.

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

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

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

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.

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

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

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

Implementa uma operação de crédito integralmente paga em dia, com sistema Price, sem inadimplência, sem renegociação e sem PDD.

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

Unlimited credit works

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

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

DEBT DESTROYER - Avalanche Method Tracker

Mpesa data useful for credit risk modelling

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.