Lucas Zaguini

Developed an automation solution to process highly complex accounting artifacts involving reconciliation schemas from Santander and Stellantis Bank. The Santander Accounting Model required complete matching algorithms to ensure account quality and compliance.

To address this, I built a desktop application that automated the reconciliation process end-to-end, delivering a user-friendly experience for financial teams. The solution significantly reduced manual effort and saved countless working hours.

The system was implemented in Python using Pandas for data manipulation and Tkinter for the user interface.

Created an automation solution that scraped, aggregated, and analyzed insurance-related data from multiple official Brazilian sources, including the National Claims Registry (RNS), SERASA, and Receita Federal. The system integrated external records with the insurer's internal Guidewire data, applying business rules and machine learning-based logic to identify high-risk fraud cases.

The solution enabled proactive fraud detection, optimized investigation prioritization, and significantly reduced financial losses by $7 million.

Designed and implemented a UiPath automation to manage customer delinquency cases for a financial institution. The solution evaluated defaulting accounts, identified guarantors associated with each customer, and executed automated fund transfers from guarantor accounts to the bank in accordance with contractual terms.

This was a highly sensitive automation initiative that required strict validation, error handling, and compliance controls. Leveraging UiPath's REFramework and Orchestrator, the system achieved secure execution, traceability, and operational scalability.