Debt collection firms rely on accurate cash flow predictions to assess whether portfolio performance aligns with initial acquisition assumptions. Traditional forecasting methods, often based on aggregated historical data, fail to capture the nuances of individual transactions within a portfolio. This can lead to discrepancies between expected and actual recovery rates, affecting financial planning and operational strategies.
A leading Polish debt collection firm sought to improve its ability to predict recoveries from owned portfolios, ensuring that financial projections remained aligned with initial estimates. The challenge was to build a machine learning model capable of analyzing transaction-level data rather than relying on aggregated portfolio metrics. The goal was to create a predictive system that functions as a controlling tool, offering precise insights into future cash flow trends.

To meet these objectives, we developed an advanced deep learning model designed to forecast cash flow at a granular level. Unlike conventional models that rely on aggregated data, our approach focused on transaction-level analysis, allowing for greater accuracy and flexibility in predicting future recoveries.
The first step involved defining a complex validation framework, ensuring that predictions accounted for portfolio-specific characteristics. Weighted validation techniques were implemented to reflect the importance of different cases and portfolios, enhancing the model’s ability to provide reliable insights across various debt types.
A deep learning architecture was chosen for its ability to identify intricate patterns in large datasets. The model was trained using historical transaction data, incorporating variables such as debtor behavior, payment history, and external economic indicators. Custom loss functions were developed to align the model’s objectives with real-world business needs, ensuring that predictions were both accurate and actionable.
We also modified open-source libraries to fine-tune model performance, optimizing key aspects of the forecasting process. Hyperparameter tuning was conducted to refine model accuracy, followed by the development of final models capable of delivering stable, high-confidence cash flow predictions.
To support decision-making, we provided a detailed analytical report summarizing model performance and key insights. This report allowed the client to assess the effectiveness of the AI-driven approach and determine the next steps for further development and integration into their financial control systems.

The machine learning model was positively evaluated for its potential to enhance the firm’s ability to predict future cash flow from debt portfolios, serving as a valuable tool for financial planning. By utilizing transaction-level data instead of aggregated figures, the model demonstrated its capacity to provide more precise recovery forecasts, which could reduce uncertainty in financial projections if deployed in production.
The application of weighted validation techniques showed promise in enabling the model to adapt to different portfolio structures, ensuring accurate predictions across various debt types. Additionally, the custom-built loss function optimized the model’s predictions to better align with business priorities, enhancing its suitability for real-world application.
The client received a comprehensive report detailing the model’s performance, equipping them with insights for potential future enhancements. The project showcased the transformative potential of AI in debt valuation and recovery strategies, illustrating how a scalable, data-driven approach could improve financial forecasting.
If implemented in production, this AI-driven solution could provide the firm with a powerful tool for financial planning and portfolio management. Its ability to generate accurate, data-driven insights has the potential to enhance decision-making and improve financial control in the highly dynamic debt collection industry.

The project began with defining a complex validation framework that accounted for portfolio-specific weights and case importance. Data preparation involved selecting and structuring transaction-level inputs to ensure accurate model training.
The initial deep learning model was developed and fine-tuned through extensive hyperparameter optimization. Custom modifications to open-source libraries were implemented to align the model with the unique needs of the debt collection industry. The final models were rigorously validated before preparing a detailed report summarizing insights and next steps for potential system integration.

A deep learning approach was used to analyze complex transactional patterns and predict future cash flow trends. A custom loss function was designed to optimize forecasting performance based on real-world debt recovery objectives. Weighted validation techniques were applied to ensure the model’s effectiveness across diverse portfolio types. Open-source libraries were modified to fine-tune model performance, and hyperparameter tuning was conducted to maximize predictive accuracy.