📈 Finance Transformation & Automation | FP&A | Finance Analytics

Finance professional with 15+ years redesigning how finance organizations operate — replacing fragmented spreadsheet processes with governed data platforms, forecasting intelligence, and AI-enabled analytics.

Combines deep finance expertise across IFRS reporting, FP&A, R2R/O2C/P2P, and executive planning and decision support with hands-on delivery of scalable finance analytics architectures using Python, PostgreSQL, Power BI, Streamlit, and machine learning.

Built and deployed a complete Finance Analytics Platform — publicly live — demonstrating end-to-end transformation from financial model to interactive decision-support application. Currently extending the platform with AI-powered financial decision support, including anomaly-aware controls, intelligent forecast overlays, and executive financial summaries.

Financial Modeling → Data Warehouse → Python Automation → Forecasting → BI Dashboards.

📌 Financial Modeling & FP&A
Valuation (DCF, Multiples), integrated financial statements (P&L, Balance Sheet, Cash Flow), forecasting, budgeting, scenario analysis, KPI frameworks, capital allocation.

📌 Finance Data & Analytics
Financial data pipelines, data warehousing, IFRS reporting structures, financial forecasting models, scenario simulation, variance analysis.

📌 Programming & Data Engineering
Python (Pandas, NumPy, SQLAlchemy, statsmodels), R, SQL, Excel (advanced), Power Query, ETL/ELT pipelines.

📌 Data Science & Forecasting
Statistics, time-series modeling, SARIMA models, machine learning applications in finance, predictive analytics.

📌 Business Intelligence & Visualization
Power BI, Tableau, dashboard design, financial performance monitoring, executive reporting.

📊 Digital Finance Forecasting & Analytics Platform

End-to-end Finance Data Stack that integrates financial modeling, data engineering, statistical forecasting, and business intelligence into a unified analytics platform.

Key components:

• Integrated IFRS 3-Statement financial model (Excel)
• Financial Data Warehouse (PostgreSQL Star Schema)
• Python automation pipeline for financial reporting
• Statistical forecasting models (SARIMA) and scenario simulation
• Power BI executive dashboards

The project demonstrates how modern finance teams can move from spreadsheet-based reporting to scalable analytics-driven FP&A systems.

📈 Volatility Forecasting with HAR-Type Models

Master’s thesis in Finance (University of Minho) investigating financial market volatility forecasting using HAR-type econometric models implemented in R.

The research evaluates 16 variations of HAR-based models applied to FTSE-100 realized volatility, incorporating jumps, signed jumps, realized semivariance, and leverage effects, with model performance assessed through multiple loss functions and statistical validation techniques.

💰Bankruptcy Prediction

Predicting corporate financial distress using machine learning and financial data analysis. Implements Random Forest, XGBoost, SVM, and more to classify bankrupt vs. non-bankrupt companies.

🏦Bank Customer Churn Prediction

Predicting customer churn in the banking sector using machine learning and customer data analysis. Models compared include Random Forest, XGBoost, and SVM, with an emphasis on optimizing recall over accuracy for better retention strategies.

📡Telecom Customer Churn Prediction

Predicting customer churn in the telecom industry using machine learning and feature selection techniques. Implements Random Forest, XGBoost, and CatBoost, with data balancing (SMOTE, undersampling) and hyperparameter tuning to maximize recall and identify at-risk customers.

🚀Provento-Manager

A web application for startup acceleration and mentorship management.

📩 Email: gustavo.provento@gmail.com
💼 LinkedIn: linkedin.com/in/gustavomfreitas
📂 GitHub: github.com/gustavomfreitas
🌍 Provento Gestor: gustmf.pythonanywhere.com