MLOps-Driven Digital Transformation of Credit Risk Assessment in FinTech Through an Adaptive Champion–Challenger Framework

Abstract

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments.