Enhancing unsecured NPL management with automatic document classification and data extraction
How Altilia can set tailored extraction classes to identify accurate debt cut-off times.
The background
The inefficiency in the manual extraction of data from unsecured Non-Performing Loan (NPL) documents adversely affects the business operating costs and hampers the identification of accurate debt cut-off timing, negatively impacting the enforcement of the claim at the judicial stage.
Pain points
The manual extraction of data from unsecured NPL documents is time-consuming and error-prone, increasing operational costs and slowing down the process of debt recovery.
The inefficiencies in data extraction hinder the identification of accurate debt cut-off timing, potentially causing claims to expire and delaying enforcement during the judicial process.
The reliance on external vendors for data processing results in high costs, limiting scalability and impacting the overall cost-efficiency of the NPL management process.
The decision to innovate
The customer aims to improve the ability and speed of data extraction to attain accurate and timely debt cut-off times, facilitating their enforcement during the judicial process.
Results & Benefit
- ~85% reduction in human errors
- 70~80% reduction in external vendor data processing costs
- 70~80% faster and more informed decision-making
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A major Italian bank specializing in financial services for businesses, particularly in the areas of factoring, lending, and credit management. It focuses on providing tailored solutions to small and medium-sized enterprises (SMEs), supporting their cash flow and growth. With a strong presence in Italy and international operations, it serves both corporate clients and institutional investors. The bank is known for its expertise in distressed credit and financing solutions, offering flexible financial products.
