End-to-end automated financial models providing deeper insights and faster execution

Still working with Excel? Python-based or hybrid financial modeling offers many advantages that can empower organizations to build more robust financial models for decision-making processes.

Python's scalability enables efficient handling of large datasets and complex calculations, while automation capabilities reduce errors and save time in model maintenance. Moreover, Python provides advanced statistical functions and flexibility for customized financial modeling solutions that seamlessly integrate with your day-to-day requirements for analysis.

Whether to automate your Net Asset Value (NAV) calculations, build powerful predictive financial forecasting models with Machine Learning or develop big data based Macro market analyses, I can support you along the way. 

1. End-to-end solution developed with Azure cloud platform that handles:
   1. Automated loading of external data and user input defined via Microsoft Power App
   2. Scalable financial modelling via Python API that can handle large datasets, as well as extend its dimensionality as required
   3. Powerful visual analyses in Power BI with automatic data updates from model runs

Decrease in risk of human error as compared to excel-based solutions.

Reduction in time required to run new scenarios as compared to excel-based solutions.

Decrease in time needed to review the final number as compared to excel-based solutions.

1. Set-up of microservices based APIs for handling extract, transform, load (ETL) operations from different sources of external data
2. Design a Power App capable for end user interaction and recording of user-defined data input
3. Develop Python REST API for reading external and user-dynamic input from 1 and 2 to perform powerful financial calculations. 
4. Create Power BI dashboards and reports for reporting and analysis on output data