Financial Engineering

Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering ArXiv ID: 2402.03806 “View on arXiv” Authors: Unknown Abstract This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making. The rapid evolution of Artificial Intelligence (AI) in finance has necessitated a balance between sophisticated algorithmic decision-making and the need for transparency in these systems. The focus is on how AutoML can streamline the development of robust machine learning models for credit scoring, while Explainable AI (XAI) methods, particularly SHapley Additive exPlanations (SHAP), provide insights into the models’ decision-making processes. This study demonstrates how the combination of AutoML and XAI not only enhances the efficiency and accuracy of credit decisions but also fosters trust and collaboration between humans and AI systems. The findings underscore the potential of explainable AutoML in improving the transparency and accountability of AI-driven financial decisions, aligning with regulatory requirements and ethical considerations. ...

Special Purpose Vehicles and Securitization ArXiv ID: ssrn-3884260 “View on arXiv” Authors: Unknown Abstract This paper analyzes securitization and more generally ?special purpose vehicles? (SPVs), which are now pervasive in corporate finance. The first part of the pap Keywords: Securitization, Special Purpose Vehicles (SPVs), Corporate finance, Asset transfer, Financial engineering, Structured Products Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper includes theoretical modeling to analyze SPV motivations and sustainability, indicating moderate-to-high mathematical complexity, while testing its implications with unique data on credit card securitizations shows strong empirical rigor. flowchart TD A["Research Question: Why do firms use SPVs and what is their economic impact?"] --> B{"Methodology"} B --> C["Data: SEC Filings & Financial Databases"] B --> D["Models: Regression Analysis and Event Studies"] C --> E{"Computational Process"} D --> E E --> F["Statistical Analysis of Asset Transfers & Risk Metrics"] F --> G["Key Findings & Outcomes"] G --> H["SPVs optimize capital structure and reduce financing costs"] G --> I["Asset transfer resolves information asymmetries"] G --> J["Risk segmentation creates efficient structured products"]