From Data to Decision: Predicting Credit Default with AI in the SAP Ecosystem

In the ever-evolving landscape of financial risk management, the accuracy of credit risk models is paramount. Financial institutions rely on robust data to calculate key metrics, such as the Probability of Default (PD), which is a core component of Basel III compliance. However, historical default data, especially when managed in systems like SAP Bank Analyzer (BA) and its Source Data Layer (SDL), can be plagued by inaccuracies or anomalies—also known as outliers. These outliers, if not properly managed, can skew statistical analysis and undermine the reliability of PD calculations. This article explores how financial institutions can leverage AI-powered automatic outlier detection to analyze historical default data within the SAP BA context, thereby improving data quality and bolstering the statistical confidence of their credit risk models. The Challenge of Historical Default Data Historical default data, which forms the basis for PD calculations, is often noisy. It can contain data entry errors, system glitches, or unusual events that don't represent the typical credit behavior of a portfolio. According to Basel III principles, the historical data used for model calibration must be representative and consistent. Outliers can violate this principle, leading to biased model outcomes and potentially inaccurate capital requirements. SAP Bank Analyzer's SDL is a powerful tool for storing and managing this data. Its granular structure allows for the detailed historization of events, including payment defaults. However, the sheer volume and complexity of this data make manual outlier detection an inefficient and often impossible task. This is where AI and machine learning come into play. Integrating AI for Automatic Outlier Detection By integrating an AI model for outlier detection directly with the data streams from SAP BA's SDL, financial institutions can automate the identification of anomalous data points. This process involves the following key steps: Data Extraction: Extract relevant historical default data from SAP BA's SDL, including key fields like customer ID, default date, payment history, and other attributes used in PD modeling. Model Training: Train a machine learning model, such as an Isolation Forest or Local Outlier Factor (LOF) algorithm, on a representative sample of historical data. The model learns the "normal" patterns of payment behavior and identifies data points that deviate significantly from these patterns. Automatic Detection: The trained model can then be applied to new or existing datasets from the SDL. It automatically flags potential outliers, which are then routed for review by a data analyst or risk officer. Data Cleansing: The identified outliers are analyzed and either corrected (if they are due to data entry errors) or removed (if they represent non-representative events). This targeted data cleansing improves the overall quality and reliability of the dataset. By systematically identifying and addressing these anomalies, the statistical properties of the historical data are enhanced, leading to more robust and accurate PD estimations. "In the landscape of financial risk, the accuracy of credit models is paramount; AI-powered outlier detection is no longer an option, but a necessity for statistical confidence." Extending AI Techniques for PD Forecasting Beyond outlier detection, a range of AI and machine learning techniques can be applied to historical default data from SAP Bank Analyzer's SDL to improve both the analysis of past defaults and the forecasting of future Probability of Default. The following methods are particularly effective: Machine Learning for Classification and Regression Supervised Learning for PD Forecasting: Algorithms like Logistic Regression, Random Forest, and Gradient Boosting Machines (e.g., XGBoost) can be trained on historical data to predict the probability of a future default. The model learns the relationship between a borrower's attributes (e.g., payment history, credit score, loan amount) and their likelihood of defaulting. These models can be integrated to provide a more accurate and dynamic PD forecast than traditional statistical methods. Support Vector Machines (SVM): Useful for classifying borrowers into "default" and "non-default" categories by finding the optimal hyperplane that separates the two classes in a high-dimensional space. Advanced Time Series Analysis Neural Networks and Recurrent Neural Networks (RNNs): For historized data in the SDL, these models are ideal for analyzing sequential patterns. An RNN can capture dependencies over time, making it particularly powerful for identifying trends in payment behavior and predicting when a borrower's financial health is deteriorating toward a potential default. Clustering for Portfolio Segmentation Unsupervised Learning (e.g., K-Means, DBSCAN): As described in the book "Introducing AI with SAP Integrated Business Planning," clustering techniques can be applied to group borrowers with similar characteristics or risk profiles. This helps a financial institution to segment its portfolio and apply different risk models or strategies to each group. This process reveals hidden patterns and provides a more granular view of credit risk. For example, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is especially effective at finding clusters of varying shapes and sizes in data, which can be useful for identifying micro-segments of risk. Leveraging SAP BTP for Public Cloud Integration To facilitate this AI-driven process and integrate diverse data sources, financial institutions can utilize the SAP Business Technology Platform (SAP BTP). The BTP provides a public cloud architecture that is ideal for connecting internal and external data. Internal Data: SAP BTP can seamlessly connect to SAP Bank Analyzer's SDL, pulling historized default data via secure APIs. External Data: The platform's integration services allow for the connection of external sources, such as credit bureaus, market data providers, or other third-party databases containing information about payment defaults or credit events. This allows for a richer, more comprehensive view of customer behavior. This hybrid approach—using the on-premise SAP BA as the system of record for critical financial data and leveraging the cloud-native capabilities of SAP BTP for advanced analytics and integration—creates a powerful, scalable solution. The AI model for outlier detection can be deployed and run within SAP BTP's environment, processing data streams from both internal and external sources. "By bridging SAP Bank Analyzer’s SDL with the public cloud via SAP BTP, institutions transform raw historical noise into a strategic asset for Basel III compliance." The Competitive Advantage of SAP's Dominance SAP’s dominant market position is a significant competitive advantage in this context. With 70% of the world's GDP and the majority of Fortune 500 companies running on SAP systems, there is an unparalleled wealth of business data within its ecosystem. This gives companies using SAP a unique opportunity. They are not just using a software platform; they are part of a massive, data-rich network. For financial institutions, this means their internal data within SAP Bank Analyzer is already aligned with the financial processes of a vast number of corporate clients. This consistency can be a major asset when building predictive models. The ability to integrate this internal data with other sources via SAP BTP, and then analyze it with advanced AI, further solidifies this competitive edge. It enables more precise risk modeling, more accurate PD estimations, and ultimately, better capital management and business decisions. "SAP's dominance means 70% of global GDP flows through its systems; leveraging this data with Machine Learning provides an unparalleled competitive edge in PD forecasting." Connect and Stay Informed: Join the Conversation: Connect with fellow professionals in the SAP Banking Group on LinkedIn. https://www.linkedin.com/groups/92860/ Stay Updated: Subscribe to the SAP Banking Newsletter for the latest insights. https://www.linkedin.com/newsletters/sap-banking-6893665983048081409/ Join my readers on Medium where I explore Capital Optimization in depth. Follow for actionable insights and fresh perspectives https://medium.com/@ferran.frances Explore More: Visit the SAP Banking Blog for in-depth articles and analyses. https://sapbank.blogspot.com/ Connect Personally: Feel free to send a LinkedIn invitation; I'm always open to connecting with like-minded individuals. ferran.frances@gmail.com I look forward to hearing your perspectives. Kindest Regards, Ferran Frances-Gil. #SAPBanking #FinancialRisk #AIinFinance #SAPBTP #MachineLearning #BaselIII #CreditRisk #DataScience #DigitalTransformation #FinTech #CapitalOptimization #SAPIBP #S4HANA #BigData #RiskManagement #FerranFrances