Audit of algorithmic intelligence

The auditing of predictive models in finance requires balancing rigorous quantitative assessment with robust algorithmic governance. This chapter examines the evaluation of advanced machine learning models—LSTM, DMLP, CNN, and XGBoost—applied to financial markets, including the FTSE CSE Morocco 15 (2018–2023), the S&P 500, and the STOXX Europe 600. Models were assessed using both quantitative metrics (R 2 , RMSE, MAE) and qualitative controls to ensure transparency, reliability, and regulatory compliance, which are critical for investor and regulator trust. Empirical results indicate that XGBoost achieved the highest stability and accuracy (R 2 up to 0.91; RMSE standard deviation < 3%), followed by LSTM (R 2 up to 0.88; standard deviation ≤ 4%), while CNN and DMLP exhibited higher variability (6%–7%) and overfitting. Bias audits and explainability analyses using SHAP and LIME confirmed the absence of systematic deviations and highlighted trade-offs between interpretability and performance. A criticality matrix based on ISO 31000/23894 standards guided the prioritization of mitigation plans, supporting operational deployment with continuous monitoring, drift detection, and periodic recalibration. This study provides a comprehensive framework for auditing predictive models in finance, integrating technical evaluation with governance principles to enhance transparency, trust, and accountability in algorithmic decision-making.