Automated Monitoring and Validation of Machine Learning Models in Credit Scoring

In the contemporary financial landscape, the integration of modern machine learning models and neural networks has emerged as a pivotal strategy in revolutionizing credit risk assessment. This paradigm shift away from traditional methodologies like logistic regression or decision trees stems from the inherent limitations of these approaches when confronted with vast and heterogeneous datasets. In stark contrast, neural network models exhibit remarkable capabilities in training on diverse data, thereby enabling superior pattern recognition, prediction accuracy, and consequent profitability for financial institutions. However, the increasing complexity of these models necessitates equally sophisticated monitoring and validation systems to effectively manage associated risks. This master thesis endeavors to address this imperative need by proposing the development of automated monitoring and validation systems tailored specifically for machine learning models and neural networks deployed in credit scoring applications. The overarching objective is to mitigate operational risks while concurrently enhancing efficiency and reliability in credit risk assessment processes. Leveraging theoretical insights and real-world banking scenarios, the research aims to design and implement comprehensive systems capable of continuous evaluation and identification of model performance issues, data drift, and validation results within the domain of credit scoring. Key objectives encompass an in-depth analysis of existing monitoring methods, a thorough understanding of institutional needs, the formulation of robust monitoring methodologies, implementation, and rigorous testing of these methodologies, and a comprehensive evaluation of their efficacy post-implementation. The ultimate aspiration is to elevate credit scoring processes, laying a robust foundation for future advancements in risk monitoring while offering a systematic approach to model and data management. From a scientific standpoint, the research contributes by pioneering the development and validation of a methodology for automated monitoring and validation of machine learning models in credit scoring applications. This involves the innovation of data processing approaches, refinement of anomaly detection algorithms, and the formulation of statistical models capable of accommodating macroeconomic fluctuations and shifts in borrower behavior. From a business perspective, the proposed automated system promises to mitigate risks, enhance operational efficiency, and bolster partner confidence through accurate and timely credit decisions. By ensuring model relevance and accuracy, the system aims to reduce credit losses and streamline decision-making processes, thereby augmenting overall system performance and resilience. Practically, the envisioned system holds immense potential in elevating credit decision quality for partner banks, optimizing resource utilization, expediting decision-making, and fostering trust in the credit assessment process. By offering real-time model monitoring capabilities, the system seeks to usher in a new era of transparency and reliability in credit risk management practices, thereby reinforcing stakeholder confidence and operational effectiveness.