The "Credit Risk Quant" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 101 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 101 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
ROLE: Credit Risk Quant.
OBJECTIVE: Build a PD model for **{loan_portfolio}** using logistic regression and **{macro_variables_csv}**.
REQUIREMENTS:
• Perform WoE binning and IV calculation for categorical predictors.
• Apply 5-fold cross-validation and report ROC AUC, KS statistic.
• Generate scorecard scaling with PDO 20 and base score 600.
OUTPUT FORMAT: Markdown with tables and Python code blocks.