GenAI in Credit Risk Modelling – Practical Applications for Modellers, Validators and Risk Analysts

Credit risk professionals spend a significant part of their time on activities that are essential but often repetitive and time-consuming: documentation, coding, data analysis, validation support, regulatory interpretation and stakeholder communication. While Generative AI is rapidly transforming knowledge-intensive work, many modelling teams are still exploring how to move from experimentation to tangible value.

This hands-on programme focuses on the practical use of GenAI across the credit risk modelling lifecycle. Participants will learn how to use AI as an analytical assistant, coding partner, documentation co-author and validation challenger while maintaining appropriate professional judgement and regulatory standards. Through interactive exercises and realistic risk modelling examples, the course demonstrates where AI can accelerate work, where human expertise remains irreplaceable, and how to integrate GenAI into everyday modelling activities.

Built around practical use cases from credit risk modelling, IFRS 9, IRB and model validation, the programme is designed for professionals seeking immediate productivity gains and practical implementation ideas.

Agenda highlights

• Understanding the AI-Enabled Risk Modeller: Identifying high-value use cases, Separating hype from practical applications, Mapping AI opportunities across the model lifecycle
• Prompt Engineering for Credit Risk: The Perse RACE framework, Developing prompts for PD, LGD, EAD and IFRS 9 analyses, Structuring complex analytical tasks for GenAI
• AI-Assisted Data Analysis and Feature Engineering: Data quality assessment, Hypothesis generation, Feature engineering support, Analytical interpretation and insights
• Coding with GenAI: Python, SQL and SAS applications, Automated code generation and review, Monitoring and reporting automation, Best practices for AI-assisted development
• GenAI for Model Documentation: Model Development Documents, Validation Reports, Executive Summaries, Regulatory responses and documentation governance
• AI-Supported Validation Techniques: Challenging assumptions, Generating validation tests, Benchmarking methodologies, Identifying model weaknesses and risks
• Responsible and Compliant Use of GenAI: Regulatory expectations, Data confidentiality considerations, Human oversight requirements, Governance good practices
• Building a Personal AI Playbook: Individual productivity workflows, Prompt libraries, Quick-win implementation opportunities, 30-day action plan

Training formula

Practical takeaways:

By the end of this course, participants will be able to:

• Identify practical GenAI use cases across credit risk modelling activities.
• Create effective prompts for modelling, validation and documentation tasks.
• Use AI to accelerate data analysis, coding and documentation preparation.
• Apply GenAI to support model validation and methodological challenge processes.
• Recognise limitations, risks and governance requirements associated with AI usage.
• Build personal workflows that improve productivity while maintaining professional judgement.

Participants will gain practical expertise that can be immediately applied to:

• Model development projects
• Model validation exercises
• IFRS 9 and IRB initiatives
• Code development and review processes
• Documentation preparation
• Model monitoring activities
• Regulatory and audit engagements

Target audience

This course is designed for credit risk professionals involved in the development, validation, monitoring or maintenance of risk models who want to leverage GenAI to improve productivity and effectiveness.

Explore the full programme

For a detailed training specification contact us at contact@perseinstitute.com.

The Perse Learning Experience

Interested?