Credit

Multimodal Insights into Credit Risk Modelling: Integrating Climate and Text Data for Default Prediction ArXiv ID: 2601.00478 “View on arXiv” Authors: Zongxiao Wu, Ran Liu, Jiang Dai, Dan Luo Abstract Credit risk assessment increasingly relies on diverse sources of information beyond traditional structured financial data, particularly for micro and small enterprises (mSEs) with limited financial histories. This study proposes a multimodal framework that integrates structured credit variables, climate panel data, and unstructured textual narratives within a unified learning architecture. Specifically, we use long short-term memory (LSTM), the gated recurrent unit (GRU), and transformer models to analyse the interplay between these data modalities. The empirical results demonstrate that unimodal models based on climate or text data outperform those relying solely on structured data, while the integration of multiple data modalities yields significant improvements in credit default prediction. Using SHAP-based explainability methods, we find that physical climate risks play an important role in default prediction, with water-logging by rain emerging as the most influential factor. Overall, this study demonstrates the potential of multimodal approaches in AI-enabled decision-making, which provides robust tools for credit risk assessment while contributing to the broader integration of environmental and textual insights into predictive analytics. ...

Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia ArXiv ID: 2510.16066 “View on arXiv” Authors: Chun Chet Ng, Wei Zeng Low, Jia Yu Lim, Yin Yin Boon Abstract Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. First, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end-to-end data extraction and machine learning credit scoring. Second, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Finally, we will release the anonymised bank transaction dataset to facilitate further research on MSME financial inclusion within Malaysia’s emerging economy. ...

Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks ArXiv ID: 2510.07444 “View on arXiv” Authors: Albert Di Wang, Ye Du Abstract Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios. ...

Towards modelling lifetime default risk: Exploring different subtypes of recurrent event Cox-regression models ArXiv ID: 2505.01044 “View on arXiv” Authors: Arno Botha, Tanja Verster, Bernard Scheepers Abstract In the pursuit of modelling a loan’s probability of default (PD) over its lifetime, repeat default events are often ignored when using Cox Proportional Hazard (PH) models. Excluding such events may produce biased and inaccurate PD-estimates, which can compromise financial buffers against future losses. Accordingly, we investigate a few subtypes of Cox-models that can incorporate recurrent default events. Using South African mortgage data, we explore both the Andersen-Gill (AG) and the Prentice-Williams-Peterson (PWP) spell-time models. These models are compared against a baseline that deliberately ignores recurrent events, called the time to first default (TFD) model. Models are evaluated using Harrell’s c-statistic, adjusted Cox-Sell residuals, and a novel extension of time-dependent receiver operating characteristic (ROC) analysis. From these Cox-models, we demonstrate how to derive a portfolio-level term-structure of default risk, which is a series of marginal PD-estimates at each point of the average loan’s lifetime. While the TFD- and PWP-models do not differ significantly across all diagnostics, the AG-model underperformed expectations. Depending on the prevalence of recurrent defaults, one may therefore safely ignore them when estimating lifetime default risk. Accordingly, our work enhances the current practice of using Cox-modelling in producing timeous and accurate PD-estimates under IFRS 9. ...

What Can 240,000 New Credit Transactions Tell Us About the Impact of NGEU Funds? ArXiv ID: 2504.01964 “View on arXiv” Authors: Unknown Abstract Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and value chain position of the firms. The empirical evidence confirms that public procurement awards significantly increase new lending, with NGEU-funded contracts generating stronger credit expansion than traditional procurement during the recent period. The results show that the impact of NGEU procurement programs aligns closely with historical procurement impacts, with differences driven mainly by lower utilization rates. Moreover, integrating high-frequency financial data with procurement records highlights the potential of Big Data in refining public policy design. ...

A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios ArXiv ID: 2410.02846 “View on arXiv” Authors: Unknown Abstract We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of spatio-temporal frailty effects. ...

Gas, Guns, and Governments: Financial Costs of Anti-ESG Policies ArXiv ID: ssrn-4123366 “View on arXiv” Authors: Unknown Abstract We study how restricting intermediary contracting over ESG policies distorts financial market outcomes. In 2021 Texas prohibited municipalities from hiring bank Keywords: ESG policies, intermediary contracting, financial market distortion, regulatory impact, municipal finance, Credit Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s primary analysis relies on standard event-study regressions and difference-in-differences methodology applied to municipal bond data, requiring significant data processing and implementation, but the mathematical depth is limited to basic econometric models. flowchart TD A["Research Question<br>Impact of ESG restrictions<br>on municipal financing"] --> B["Methodology<br>Event Study + Difference-in-Differences"] B --> C["Data Sources"] C --> D["Municipal Bond Data"] C --> E["Bank Contracting Data"] C --> F["Texas Policy 2021"] D & E & F --> G["Computational Process<br>Estimate spread changes<br>& loan pricing impacts"] G --> H["Key Findings"] H --> I["+8-10 bps spread increase<br>in Texas municipal bonds"] H --> J["Higher borrowing costs<br>for municipalities"] H --> K["Market distortion<br>from ESG restrictions"]

Theories of Financial Inclusion ArXiv ID: ssrn-3526548 “View on arXiv” Authors: Unknown Abstract This article presents several theories of financial inclusion. Financial inclusion is defined as the availability of, and the ease of access to, basic formal fi Keywords: Financial Inclusion, Formal Finance, Economic Development, Banking Accessibility, Credit Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual review that categorizes existing theories of financial inclusion without presenting new mathematical models or empirical data analysis. It focuses on theoretical frameworks and policy discussions rather than quantitative methods or backtesting. flowchart TD A["Research Goal: Explore Theories of Financial Inclusion"] --> B["Methodology: Literature Review of Key Theories"] B --> C["Data: Academic Papers &amp; Economic Studies"] C --> D["Computational Process: Analysis of Access Barriers &amp; Impacts"] D --> E{"Outcomes"} E --> F["Theory 1: Supply-Side Constraints"] E --> G["Theory 2: Demand-Side Barriers"] E --> H["Theory 3: Institutional Frameworks"] F & G & H --> I["Key Finding: Link between Formal Finance &amp; Economic Development"]

Fintech for Financial Inclusion: A Framework for Digital Financial Transformation ArXiv ID: ssrn-3245287 “View on arXiv” Authors: Unknown Abstract Access to finance, financial inclusion and financial sector development have long been major policy objectives. A series of initiatives have aimed to increase a Keywords: Financial Inclusion, Access to Finance, Financial Sector Development, Microfinance, Credit Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a policy-oriented framework discussing regulatory strategies and digital infrastructure, lacking mathematical formulas or statistical models; its empirical support relies on high-level case studies (e.g., India, Kenya) and aggregated data from sources like the World Bank, with no backtesting or implementation details. flowchart TD A["Research Goal:<br/>Framework for Digital Financial Transformation"] --> B["Data & Inputs:<br/>Policy Initiatives & Microfinance Data"] B --> C["Methodology:<br/>Thematic Analysis & Synthesis"] C --> D["Computational Process:<br/>Mapping Inclusion to Digital Tech"] D --> E["Key Findings:<br/>Fintech as Catalyst for<br/>Financial Sector Development"]