Investment Bank Reduces AI Risk 70% with Comprehensive Data Governance

A global investment bank was expanding AI usage across trading strategies, credit risk assessment, and client advisory. However, regulators expressed concerns about model transparency—specifically, the inability to explain what data trained models and how that data influenced decisions.

An internal audit revealed significant gaps: training data sources were inconsistently documented, data quality issues had propagated into models without detection, and there was no systematic way to assess whether training data introduced biases.

ELMET designed a comprehensive data governance framework for AI. Data lineage tracking was implemented from source systems through feature engineering to model training, creating complete provenance documentation for every model.

Automated data quality monitoring was deployed across all AI data pipelines. Quality rules validated completeness, consistency, and accuracy. Anomaly detection identified data drift before it impacted models. Issues triggered alerts and blocked model updates until resolved.

Bias detection capabilities were added to the data pipeline. Statistical tests identified potential bias in training data across protected characteristics. Fairness reports were generated automatically and reviewed before model deployment.

Stakeholder reporting dashboards provided transparency to different audiences. Regulators received compliance-focused views with audit trails. Business leaders saw risk metrics and model performance. Data scientists accessed technical quality metrics. The single source of truth eliminated conflicting reports and built trust across all stakeholders.