Enhancing Credit Risk Evaluation with Predictive Algorithms

Client

Finwave

Market

Italy

Industry

Financial Services

Challenge

Credit institutions must enhance accuracy and efficiency in calculating expected losses to comply with IFRS9 standards.

How Dynius helped

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

The solution

Dynius partnered with Finwave to develop a tailored AI solution for predicting macroeconomic variables and credit risk metrics. The solution automates the calculation of Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), ensuring IFRS9 compliance. By integrating structured and unstructured data from sources like Central Banks, it delivers precise forecasts customized to the needs of financial 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 solution significantly improved the efficiency of credit risk evaluation processes for Italian banks. By automating manual calculations banks reduced errors and ensured regulatory compliance.