Financial institutions and data science practitioners are increasingly leveraging interpretable machine learning models to streamline credit risk assessment. A recent technical publication demonstrates a systematic approach to constructing a credit scoring grid from a logistic regression model, offering a reproducible framework for converting statistical outputs into actionable business metrics. The methodology begins by transforming logistic regression coefficients into a standardized scoring scale ranging from zero to one thousand. Each categorical variable within the model is assigned a weighted score calculated through a normalized formula that compares individual category coefficients against the maximum coefficient per variable. This process yields a client-specific total score, where higher values correspond to lower default probability. The author utilized AI-assisted coding tools to generate the underlying computational logic and visualizations, though manual verification was emphasized to ensure mathematical accuracy. Variable importance analysis reveals that loan-to-income ratio and home ownership status collectively account for the majority of score variance. Specifically, loan percent of income contributes thirty-five percent of the weight, followed by home ownership classification at thirty-one percent, loan interest rate at twenty-eight percent, and prior default history at five percent. This distribution aligns with established credit risk principles, confirming that the model effectively captures financially meaningful signals. To validate predictive performance, the scoring system was evaluated against historical default and non-default cohorts across training, testing, and out-of-time datasets. Density plots demonstrate clear separation between high-risk and low-risk populations, with defaults heavily concentrated in the lower score ranges. The model then progresses to risk grid construction by dividing scores into twenty equal segments. These segments are aggregated into six distinct risk classes based on observed default rates, adhering to strict constraints: internal risk uniformity, a minimum thirty percent differential between adjacent classes, and a baseline population threshold of one percent per tier. Stability assessments confirm that risk stratification remains consistent across time periods, with class distributions maintaining structural integrity. The resulting grid provides lenders with a transparent, audit-ready decision framework. While the current implementation relies on visual vingtile grouping, the author notes that advanced clustering techniques and Weight of Evidence transformations could further refine class boundaries in subsequent iterations. Open-source code and analytical documentation are publicly available for replication and extension. The project underscores a growing industry trend toward transparent, mathematically grounded credit scoring systems that balance predictive accuracy with regulatory compliance. By standardizing coefficient translation and risk tiering, this framework offers financial technology developers a robust foundation for deploying interpretable machine learning in consumer lending.