Successful completion of the training project on GE.C.O.

Potential New Machine Learning Predictive Models Identified for Our Credit Recovery Platform
The training internship involving three brilliant students from the University of Calabria has successfully concluded, yielding extremely positive results. The students were engaged in the development of advanced predictive models within our GE.C.O. platform, a specialized management software for credit recovery.
This educational initiative was part of a strategic project aimed at enhancing the extensive experience accumulated over time by GE.C.O., by transforming data into decision-making resources through the use of advanced machine learning algorithms.
The goal of the project was to explore new features that could support, optimize, and personalize credit recovery activities, offering clients strategic insights based on concrete data and advanced analytics.
During the internship, various data analysis methodologies were tested and implemented with the aim of:

•    Extracting descriptive models of client characteristics and their impact on financial profiling;
•    Applying classification techniques to build predictive and descriptive models for segmenting clients based on their specific traits;
•    Identifying frequent patterns and association rules to better understand user financial behaviors.

This activity successfully combined IT and financial expertise, yielding significant results in terms of customer profiling and behavioral understanding, and demonstrating the added value of data mining and machine learning technologies in the financial sector.
At the heart of the internship was the identification of recurring patterns, trends, and significant anomalies. In this context, advanced predictive models were developed to estimate both the outcome of cases and the expected recovery percentage at the end of the process. These tools helped highlight strengths, limitations, and potential practical applications, providing valuable insights for further improving management strategies within the platform.
The internship was not only a valuable learning opportunity for the young university students, but also a moment of innovation—testing predictive tools in the field that can make a real difference in a sector increasingly driven by data-based decision-making. This project marks a first step toward deeper integration between machine learning technologies and day-to-day operations, with the goal of further enhancing GE.C.O. as a proactive tool in the credit recovery domain.