Operational Readiness Assessment for ML Models in Banking Risk Applications

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Deploying machine learning models in banking risk assessment requires rigorous operational readiness evaluation to ensure reliability, performance, and compliance. This article focuses specifically on LightGBM models – increasingly popular in credit risk applications – and provides a structured framework for assessing operational readiness before production deployment.

Understanding LightGBM’s Technical Characteristics

Before establishing an operational readiness framework, it’s essential to understand LightGBM’s unique characteristics that impact production deployment:

Key LightGBM Properties Affecting Operations

1. Leaf-wise Tree Growth: Unlike XGBoost’s level-wise growth, LightGBM grows trees leaf-wise, creating deeper, more complex trees. This impacts memory usage patterns and inference latency in production.

2. Histogram-based Algorithm: LightGBM bins continuous features into discrete bins, reducing memory usage but requiring consistent binning strategies between training and inference.

3. Sparse Optimization: LightGBM efficiently handles sparse data, making it sensitive to feature engineering pipelines and data transformation…