Data Science Projects in Finance
The financial sector is one of the most demanding environments when it comes to data analysis. Real-time decision-making, risk forecasting, and the efficient management of large volumes of information make Data Science a key strategic resource.
In this section, you can explore some of the projects developed in the financial field, where I have applied Big Data, Machine Learning, and advanced analytics techniques to solve real-world problems and deliver measurable value to financial institutions, insurance companies, and accounting and treasury departments.
What types of projects have I developed?
- Default Risk Prediction Models: Development of systems capable of predicting credit risk using supervised models and historical variables.
- Profitability Analysis and Portfolio Optimization: Application of statistical techniques to analyze financial products, simulate scenarios, and propose data-driven investment strategies.
- Fraud Detection and Anomaly Identification: Anomaly detection algorithms applied to banking operations, accounting movements, and card transactions.
- Financial Reporting Automation: Creation of interactive dashboards with tools like Power BI or Tableau to monitor financial KPIs and generate real-time reports.
Tools and technologies I use
I work with languages such as Python and SQL, and platforms like Power BI, Azure, Google Cloud, or AWS, depending on the client’s tech ecosystem. The models are built using libraries like Scikit-Learn, XGBoost, or Prophet, based on the nature of the data and the goals of the analysis.
