Small and Medium-sized Enterprises (SMEs) are vital to the modern economy, yet their credit risk analysis often struggles with scarce data, especially for online lenders lacking direct credit records. This paper introduces a Graph Neural Network (GNN)-based framework, leveraging SME interactions from transaction and social data to map spatial dependencies and predict loan default risks. Tests on real-world datasets from Discover and Ant Credit (23.4M nodes for supply chain analysis, 8.6M for default prediction) show the GNN surpasses traditional and other GNN baselines, with AUCs of 0.995 and 0.701 for supply chain mining and default prediction, respectively. It also helps regulators model supply chain disruption impacts on banks, accurately forecasting loan defaults from material shortages, and offers Federal Reserve stress testers key data for CCAR risk buffers. This approach provides a scalable, effective tool for assessing SME credit risk.

Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios.

In its most recent advisory, the CFPB addressed a critical question – "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken? The answer is an obvious'Yes'. With the CFPB's circular reminding everyone of adverse action notice requirements under the ECOA Act, some credit unions find themselves in a quandary when it comes to explaining their credit decisions, which is perceived to be difficult when they use state of the art decisioning algorithms. However, modern AI solutions have moved beyond mere aspects of explainability to enable fair lending, and have gone the extra mile to remove inherent biases that may arise in data based models. Nonetheless, it is necessary to understand the CFPB's guidance and how AI can effectively be a solution itself. The use of algorithms in making lending decisions is not something novel or new. Credit risk assessment naturally requires getting your arms around as much relevant data as you can. A mix of models and algorithms have been the backbone of credit decisions for around 4 decades now, with credit analysts using financial statements, credit histories, and other data sources to estimate credit risk, set credit limits and recommend payment plans. With time, the datasets in question have become so voluminous that lenders had to move from manual methodologies to computational models for analysis of data using analytics. Recent advancements in computational methods have introduced the "AI" element in lending processes to make credit risk assessments much more accurate. Artificial Intelligence and Machine Learning models leverage a diverse set of alternate data sources beyond bureau, and use historical training data to determine non-linear correlations between data points, and provide advanced predictive signals on member behavior and lending outcomes. The unique proposition here is the ability of AI/ML models to analyze voluminous quantities of data, detect hitherto unknown correlations, and keep self-learning and adapting the models with little or no manual interventions. AI enabled technologies have helped put the spotlight on the increasingly visible disparities in existing lending processes. A 2019 paper by Robert Bartlett & Co. helps quantify this disparity: "Black and Latino applicants receive higher rejection rates of 61% compared to 48% for other races.