Fixed Income / Credit

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems ArXiv ID: 2509.02388 “View on arXiv” Authors: N. Jean, G. Le Pera Abstract Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techniques with Malle’s five category model of behavior explanation: Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization. The framework is designed to be applicable across AI assisted decision making systems, with the goal of enhancing transparency, interpretability, and user trust. We demonstrate its practical relevance through real world case studies, including credit risk assessment and regulatory analysis powered by large language models (LLMs). By aligning technical explanations with human cognitive mechanisms, the framework lays the groundwork for more comprehensible, responsible, and ethical AI systems. ...

The TruEnd-procedure: Treating trailing zero-valued balances in credit data ArXiv ID: 2404.17008 “View on arXiv” Authors: Unknown Abstract A novel procedure is presented for finding the true but latent endpoints within the repayment histories of individual loans. The monthly observations beyond these true endpoints are false, largely due to operational failures that delay account closure, thereby corrupting some loans. Detecting these false observations is difficult at scale since each affected loan history might have a different sequence of trailing zero (or very small) month-end balances. Identifying these trailing balances requires an exact definition of a “small balance”, which our method informs. We demonstrate this procedure and isolate the ideal small-balance definition using two different South African datasets. Evidently, corrupted loans are remarkably prevalent and have excess histories that are surprisingly long, which ruin the timing of risk events and compromise any subsequent time-to-event model, e.g., survival analysis. Having discarded these excess histories, we demonstrably improve the accuracy of both the predicted timing and severity of risk events, without materially impacting the portfolio. The resulting estimates of credit losses are lower and less biased, which augurs well for raising accurate credit impairments under IFRS 9. Our work therefore addresses a pernicious data error, which highlights the pivotal role of data preparation in producing credible forecasts of credit risk. ...

Investigate The ESG Score Methodology ArXiv ID: 2312.00202 “View on arXiv” Authors: Unknown Abstract Whether the Refinitiv provide a reliable and trusted methodology in the process of aggregating 10 category scores to overall score? Keywords: Credit Scoring, Methodology Validation, Refinitiv, Data Aggregation, Financial Ratings, Fixed Income / Credit Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual critique and literature review of ESG methodologies without advanced mathematical derivations, and it lacks code, backtests, or datasets, indicating low empirical rigor. flowchart TD A["Research Goal:<br>Validate Refinitiv ESG Score Methodology"] --> B{"Key Methodology Steps"} B --> C["Data Input:<br>10 Category ESG Metrics"] B --> D["Data Input:<br>Industry-Specific Weightings"] C --> E["Computational Process:<br>Normalization & Scoring"] D --> E E --> F["Computational Process:<br>Weighted Aggregation"] F --> G["Outcome:<br>Overall ESG Score (0-100)"] F --> H["Outcome:<br>Score Reliability & Methodology Assessment"]

Shadow Banking ArXiv ID: ssrn-2378449 “View on arXiv” Authors: Unknown Abstract The rapid growth of the market-based financial system since the mid-1980s has changed the nature of financial intermediation. Within the system, “shadow banks” Keywords: Shadow Banking, Financial Intermediation, Market-Based Finance, Credit Supply, Banking Sector, Fixed Income / Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper appears to be a conceptual/theoretical overview of shadow banking, focusing on definitions and systemic issues rather than formal models or backtesting. The provided excerpt suggests high-level discussion without empirical data or implementation details. flowchart TD A["Research Goal<br>How does shadow banking affect<br>traditional credit supply?"] --> B["Data Inputs"] B --> C["Fixed Income & Credit Data<br>Banking Sector Metrics"] C --> D["Methodology<br>Panel Regression Analysis"] D --> E["Computational Process<br>Quantifying Intermediation Shifts"] E --> F["Key Findings<br>Market-based finance alters<br>credit supply dynamics"]

Shadow Banking ArXiv ID: ssrn-1645337 “View on arXiv” Authors: Unknown Abstract The rapid growth of the market-based financial system since the mid-1980s changed the nature of financial intermediation in the United States profoundly. Within Keywords: Market-Based Financial System, Financial Intermediation, Shadow Banking, Credit Intermediation, Systemic Risk, Fixed Income / Credit Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper involves advanced mathematical modeling of shadow banking’s systemic risks, but also includes rigorous empirical analysis of data from financial intermediaries and markets, making it both theoretically complex and data-heavy. flowchart TD A["Research Goal: How does shadow banking reshape financial intermediation & systemic risk?"] --> B["Data/Inputs: SEC filings, CRSP, FR Y-9C, MBS/ABS issuance data (1985-2008)"] B --> C["Key Methodology: Comparative analysis of Market-Based vs. Traditional Banking"] C --> D["Computational Process: Asset growth regression & maturity transformation modeling"] D --> E["Key Finding 1: Shadow banks replicate maturity transformation but lack deposit insurance"] D --> F["Key Finding 2: Systemic risk shifts from banks to capital markets via run-prone liabilities"] E & F --> G["Outcome: Policy shift needed for prudential regulation in non-bank financial intermediation"]

Shadow Banking ArXiv ID: ssrn-1640545 “View on arXiv” Authors: Unknown Abstract The rapid growth of the market-based financial system since the mid-1980s changed the nature of financial intermediation in the United States profoundly. Within Keywords: Market-Based Financial System, Financial Intermediation, Shadow Banking, Banking Regulation, Credit Markets, Fixed Income / Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper discusses the growth of shadow banking and its macroeconomic implications, which is conceptual and theoretical rather than mathematically dense or data-heavy, lacking specific formulas, code, or backtesting details. flowchart TD A["Research Goal: Impact of Shadow Banking<br>on Financial Intermediation"] --> B["Data Collection &amp; Processing"] B --> C["Regression Analysis &amp; Modeling"] C --> D{"Key Findings &amp; Outcomes"} B --> B1["Market-Based System Metrics"] B --> B2["Shadow Banking Volume"] B --> B3["Credit Market Data"] C --> C1["Fixed Income / Credit Trends"] C --> C2["Regulatory Effectiveness Tests"] D --> D1["Reduced Bank Reliance"] D --> D2["Systemic Risk Indicators"] D --> D3["Regulatory Gaps Identified"]