Enhancing Credit Risk Evaluation with Predictive Algorithms

Client

Finwave

Market

Italy

Industry

Financial Services

Challenge

Credit institutions need to improve accuracy and efficiency in calculating expected losses in compliance with IFRS9.

How Dynius helped

Dynius developed an AI-driven predictive algorithm to automate and enhance credit risk evaluation.

The solution

Dynius collaborated with Finwave to create a custom AI-driven solution to automates the prediction of macroeconomic variables and credit risk metrics. This solution calculates Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), ensuring compliance with IFRS9 regulations. It integrates structured and unstructured data from various sources (e.g., Central Banks), providing accurate forecasts tailored to the specific needs of different financial services institutions.

Key features include:

• Predictive modeling of macroeconomic variables.
• Accurate calculation of PD, LGD, and EAD.
• Compliance with IFRS9 regulations.
• Flexibility to incorporate various external data sources.

Efficiency Gains

Automating complex calculations saved significant time and resources.

Error Reduction

The risk of manual calculation errors was drastically reduced.

Regulatory Compliance

Ensured adherence to IFRS9 regulations with accurate and flexible solutions.

Data Utilization

Enhanced data-driven decision-making by leveraging diverse external data sources.

Takeaways

The AI-driven solution developed by Dynius significantly improved the efficiency and accuracy of credit risk evaluation processes for Italian banks. By automating previously manual calculations, banks reduced errors, ensured regulatory compliance, and enhanced their strategic planning capabilities.