NPL portfolio management

Portfolio valuation relies on underwriting and valuation models that analyze numerous variables at the individual position level, including loan vintage, product type, borrower characteristics, payment history, recovery status, and historical asset performance. These analyses are supplemented by macroeconomic indicators, market benchmarks, and historical data to estimate recovery potential and define the investment's risk-return profile.

The use of advanced technologies—such as machine learning, big data analytics, and predictive models—enables the processing of vast volumes of structured and unstructured data, supporting both the acquisition phase and subsequent portfolio management. This approach allows for the identification of behavioral patterns, the optimization of asset segmentation, and improved forecasting of expected cash flows.

The SPV structure enables oversight of the entire investment lifecycle—from initial due diligence and the assessment of market opportunities to transaction closing, operational management, and continuous performance monitoring. Management and recovery strategies are constantly adapted in response to portfolio evolution and market dynamics, with the aim of maximizing value for investors.

The integration of advanced analytics, performance benchmarks, investment governance, and specialized expertise makes it possible to validate the assumptions underpinning business plans and to develop investment strategies that are sustainable, competitive, and aligned with long-term return objectives.