A leading private bank lacked a standardised AI model validation framework. CALIGO designed and implemented an end-to-end validation workflow covering requirement definition, multi-source data collection, validation testing, and automated reporting — strengthening NPL risk management and model governance.
The bank lacked a standardised and scalable validation framework. Processes were fragmented, data sources dispersed, and the organisation had limited ability to consistently evaluate model performance. Models suffered from low-quality inputs — including features with zero or near-zero variance — increasing the risk of overfitting and underfitting.
CALIGO implemented a standardised, end-to-end AI model validation framework covering requirement definition, multi-source data collection, validation testing, and automated reporting. Data preprocessing removed features with no predictive value, reducing noise and preventing overfitting. This enabled a repeatable, scalable workflow with continuous alignment through intermediate deliverables.
A standardised validation framework was established, enabling consistent and objective evaluation of model performance across the lifecycle. Validation requirements and control points were clearly defined at the algorithm level, improving transparency and governance. Automated validation and reporting significantly reduced operational effort. The project strengthened the bank's ability to measure customer risk, contributing to NPL reduction.