Task automation using programming languages like Python, R, Colab and databases like SQL Server. This improved the performance of daily process of ETL and reporting. Forecast of different balance sheet products by using econometric models and reduction models. This improved the way to make forecast of the full revenue of the company and also, the Finance unit. Daily reporting and forecast of Financial Risk and Results. This improved the financial revenue in 2 consecutive years. ALCO and on demand presentations. Risk Financial, Balance Sheet, Result and Rating ratios forecast. This was a first time construction of this kind of metrics for the finance unit, which involved built all from scratch. Creation of different Star Models for different purpose of the business. Daily reporting of strategies, financial forecast and risk financial metrics. This improved the financial management and also, improved the speed of business decision due to allow relevant information for stakeholders.

Financial Risk metrics modeling and forecast. This allowed help the finance unit to improve the regulatory requirements. Update of financial risk models to Basel III. This improved the financial management and allowed to get closer to banking standards. ALCO and on demand presentations. Yield curve modeling with CSA adjustments. This allowed to update the methodologies to most recent market standards. Financial models (e.g. Vasicek, VaR), which improved financial risk management. Employees recruitment for the Financial Risk Unit. Task automation using programming languages and database servers. This allowed to stay ahead on the most recent technologies and improved performance of process of ETL and reporting. Stability models for loans with focus in improve revenue using behavior analysis.

Implementation of dimensional reduction models to simplify analysis of composition of sovereign funds and international reserves. This boosted the financial reserves/fund financial management. VaR modeling. This improved financial management identifying tail loss scenarios. Creation of several STAR models for different purposes, which improved reporting performance on ETL and reporting. Daily automated reporting. This improved reporting performance in speed and accuracy of metrics. Monthly and on demand presentations. Financial Risk metrics modeling and forecast. This improved the variety of metrics available.

Product system maintenance and implementations, improving performance on data processing. Projects for the International Reserves and Sovereign Funds, improving accuracy on analysis. Product system maintenance implementations, improving data management and analysis. Daily automated Reporting, improving reporting performance. Investment strategy reporting, improving revenue management by forecasting analysis. Research, Modeling and forecast of Financial Risk metrics, Benchmarks, and returns of investment portfolios of Central Bank.

Product system migration, configuration and maintenance. This improved daily valuation by defining a clear step by step process. Yield curve construction from scratch for forwards, swaps, bonds. Forecast and modeling via Bootstrapping, Nelson-Siegel Svensson. Derivatives valuations adjustments like CVA, which improved measured impacts on balance of hedge accounting strategies. Model development for diverse financial products, improving financial accuracy on valuation. Financial risk metrics modeling, forecast and reporting, which improved financial risk management in short term. Automation of tasks using diverse tools like Python, SQL Server, Power BI, R, which makes improvements in performance of ETL and reporting tasks. ALCO and on demand presentations. Creation of STAR models from scratch, improving reporting performance and daily process of ETL of large volumes of market transactions and balance sheet information.