Bank-style Credit Risk Scorecard using Logistic Regression, IFRS-9 Expected Credit Loss, and an Interactive Streamlit Risk Dashboard for loan default prediction.
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Updated
Mar 10, 2026 - Jupyter Notebook
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
Coding assignments of the "Machine Learning in Finance & Insurance" course at ETH Zürich (Fall 2024).
A dirty-work toolbox for data analyses about fixed income securities, developed by only me (not the institute) as an intern data analyst.
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.
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
Implementa uma operação de crédito integralmente paga em dia, com sistema Price, sem inadimplência, sem renegociação e sem PDD.
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
Detecting credit card fraud using Random Forest on a 2023 European transactions dataset
A calibrated LightGBM credit scoring model with SHAP-based feature attribution and plain-English explanations, designed to meet EU AI Act high-risk AI requirements for transparency and the right to explanation.
With Kredy it is Easily simulate your loan with free quotes and personalized support. Find the best financing options for any project in minutes.
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.
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