Algorithmic Profiling of Operational Risk: A Data-Driven Predictive Model for Micro-Enterprise Solvency Assessment

The persistent financial exclusion of micro-enterprises is fundamentally driven by information asymmetry, as traditional credit scoring models rely heavily on audited financial statements that small entities rarely possess. To address this “thin-file” challenge, this study proposes a shift from asset-based valuation to behavioral algorithmic profiling, hypothesizing that high-frequency operational risk patterns can serve as informative proxies for solvency compared to static liquidity ratios. Using an Extreme Gradient Boosting (XGBoost) architecture on a synthetic dataset of 5000 micro-enterprise transaction logs, we develop a predictive framework that extracts latent features such as supply chain latency, inventory turnover consistency, and digital footprint intensity. The proposed model achieves an Area Under the Curve (AUC) of 0.94, outperforming traditional linear baselines and achieving performance levels above those commonly reported in micro-enterprise solvency prediction studies. The results indicate that operational stability emerges as a strong indicator of repayment capacity within the evaluated context, outperforming static liquidity-based measures. These findings suggest that computational intelligence approaches grounded in high-frequency operational data may contribute to mitigating information asymmetries in micro-enterprise credit assessment, particularly in environments characterized by limited financial disclosure, although further empirical validation is required prior to large-scale deployment.