Automation Meets Independent Validation in Credit-Risk Management ⚡

In the evolving world of credit-risk management, the number and complexity of models keep growing—making independent validation increasingly challenging. Intesa Sanpaolo’s Group Chief Risk Management area tackled this by introducing digitalization and Generative AI (GenAI) to streamline model-validation workflows, reduce manual effort, and enhance regulatory compliance. Learn more about the latest innovations in credit-risk management at our Credit Risk Summit.”

The Challenge: Rising Model Complexity

With hundreds of internal models under review, the bank faced a widening gap between portfolio growth and independent validation coverage. Manual testing, SAS-based code reviews, and repetitive Excel calculations consumed valuable expert time, slowing down reporting and limiting focus on high-value insights.

The Solution: Automation + GenAI

Intesa Sanpaolo developed a fully automated validation pipeline for credit-risk models—starting with IRB PD and LGD SME Retail models. Key GenAI applications included:

• SAS-to-Python code conversion with detailed annotations.

• Automated documentation of statistical tests and validation results.

• Regulatory assessment support, parsing thousands of pages of EBA and ECB guidelines for faster compliance checks.

This approach allowed a human-in-the-loop model, where experts review GenAI outputs, ensuring accuracy and maintaining oversight.

Key Results: Efficiency and Accuracy

• 10–15% reduction in annual validation effort.

• Improved consistency across model-validation reports.

• Faster scalability, freeing experts to focus on complex analysis and backlog reduction.

By leveraging GenAI without replacing experts, the bank accelerated digital transformation and optimized the internal validation function.

The Bigger Picture: Scaling Digital Transformation

Generative AI is not just a tool—it’s an enabler of smarter workflows. Embedding automation and AI into validation processes allows banks to cover more models with greater accuracy while focusing human talent on high-value tasks. A phased rollout with dedicated use cases helps measure impact and mitigate risks while building long-term capabilities.

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