Serialized Loan Approval Datasets

• Serialized loan approval datasets are structured repositories encoding detailed loan events with features, outcomes, and auxiliary information for ML and fairness research.
• They employ diverse serialization protocols—tabular, text-based, and temporal—to facilitate precise survival analysis, benchmarking, and model explainability.
• Key challenges include selection bias, data sparsity, and fairness concerns, which drive ongoing innovations in preprocessing, algorithm design, and ethical evaluation.

Serialized loan approval datasets are structured repositories in which individual loan applications—including features, outcomes, and auxiliary information—are encoded in a manner amenable to automated processing, benchmarking, or explanation of model decisions. These datasets are central to research and operationalization of machine learning, survival analysis, fairness evaluation, and explainability in financial credit systems. Their forms and processing protocols span from highly structured tabular models to text-serialized and survival-task–oriented pipelines, each supporting a range of research objectives and industry practices.

1. Construction and Serialization Protocols

Central to the notion of a serialized loan approval dataset is the encoding of each loan event into a discrete, self-contained record, supporting subsequent analysis. The serialization process refers to the conversion of raw or heterogeneous data—such as application forms, transaction logs, or decision outcomes—into structured formats usable by various algorithmic tools.

Prominent serialization strategies include:

• Tabular Serialization: Classical datasets record each application as a row with features such as applicant demographics, loan details, and financial indicators. Typical cleaning includes feature selection, imputation of missing values, and encoding of categorical variables numerically (e.g., label encoding) as exemplified in (Haque et al., 11 Oct 2024).
• Text-based Serialization: For LLMs and NLP models, tabular features are linearized into natural language or narrative form. Notable approaches include GReaT ("age is 32, sex is female, loan duration is 48 months, loan purpose is education") or LIFT ("A 32-year-old female is applying for a loan for 48 months for education purposes"), designed to maximize the interpretability and context integration for LLMs (Azime et al., 29 Aug 2025).
• Temporal Serialization for Survival Analysis: Survival datasets track an index event (e.g., loan approval/issuance) and an outcome event (e.g., repayment, default). The result is a tuple $(T_i, \delta_i, X_i)$, capturing duration until event/censoring, endpoint indicator, and covariates (Green et al., 7 Jul 2025). Datasets may utilize automated pipelines to match index/outcome events and compute engineered features.

The serialization strategy is tightly linked to research objectives, with text-based approaches favoring LLM benchmarks and temporal pipelines supporting survival and risk modeling over event data.

2. Representative Datasets and Feature Taxonomies

Serialized loan approval datasets draw on a taxonomy of features and explanations tailored to their ultimate use and audience.

• User-Centric Explanatory Datasets: The Xnet dataset was constructed by iterative survey design to elicit, clean, and annotate loan denial explanations that are both actionable and comprehensible to non-experts (Chander et al., 2019). Xnet includes human-endorsed feature categories: credit history (e.g., “limited credit”), employment (e.g., “unstable job”), income (e.g., “low income”), and debts (e.g., “current debt”), with 60 unique denial explanations structured to reflect actionable and empathetic guidance.
• Algorithm-ready, High-dimensional Sets: Large tabular sets (e.g., the Kaggle-derived set in (Haque et al., 11 Oct 2024)) initially feature upward of 37 attributes. Rigorous cleaning yields structured arrays of numeric and categorical features relevant to binary (approved/denied) classification.
• Transaction-based Survival Sets: FinSurvival encodes >7.5 million DeFi records with 128 engineered features per event, capturing user-level, loan-level, and macroeconomic signals. Each outcome is time-stamped for survival analysis, with censoring flags denoting incomplete observations (Green et al., 7 Jul 2025).
• DeFi Protocol Logs: Datasets collecting on-chain financial behaviors (e.g., Aave v2) are serialized by interval and position, with core features like health factor, collateralization, and borrower history suitable for machine learning models targeting position delinquency (default analogs) (Wolf et al., 2022).

3. Methodologies and Modeling Strategies

Serialized datasets facilitate a spectrum of machine learning, survival analytics, and explainability research.

• Ensemble Machine Learning (Tabular): On classical tabular forms, ensemble techniques such as AdaBoost, RandomForestClassifier, and DecisionTreeClassifier reach high classification accuracy for the approved/denied task; AdaBoost attained 99.99% accuracy on a benchmark Kaggle set (Haque et al., 11 Oct 2024). SVMs and baseline GaussianNB classifiers are also evaluated, though the latter is less performant.

Evaluation protocols use metrics such as Accuracy, Precision, Recall, F1 Score, and ROC curves, with careful attention to class balance and preprocessing pipelines (imputation, encoding, irrelevant feature removal).

• Multi-task and Gated Networks (Bias-aware): The RMT-Net and RMT-Net++ architectures couple rejection/approval classification with default/non-default prediction via a gating network. The gating weight, parameterized by $$g_i^{(j)} = \sigma(\alpha^{(j)} p_i^{(t)} + \beta^{(j)})$$, adapts cross-task information flow, exploiting the empirically positive correlation between rejection and default risk observed in Lending Club data (Liu et al., 2022). The multi-policy variant (RMT-Net++) enables handling of distinct approval strategies, boosting robustness in dynamic policy environments.
• LLM Table-to-Text Evaluation: Investigations into LLMs’ ability to process serialized datasets compare JSON, List, GReaT, LIFT, and LaTeX formats. Weighted-average F1 score is the principal evaluation metric, alongside fairness metrics (Statistical Parity and Equality of Opportunity) computed with respect to protected attributes (e.g., gender). While LLMs benefit from few-shot in-context learning (F1 improved by up to 59.6% over zero-shot), improvements in fairness are mixed, with some serialization strategies exacerbating disparities (Azime et al., 29 Aug 2025).
• Survival Analysis and Event-time Modeling: Survival tasks constructed via the FinSurvival pipeline use the Kaplan–Meier estimator:

$S(t) = \prod_{i(t_i \leq t)} (1 - \frac{d_i}{n_i})$

and the Cox model:

$\lambda(t|X) = \lambda_0(t)\exp(\beta^T X)$

to formalize time-to-repayment or default. For benchmarking, survival times are thresholded via restricted mean survival time (RMST), supporting both regression and classification protocols even in high-censoring environments (>80%) (Green et al., 7 Jul 2025).

4. Challenges in Dataset Creation and Benchmarking

Serialized loan approval datasets confront several methodological and operational challenges:

• Selection Bias and Missing-Not-at-Random Data: Default outcomes are typically available only for approved loans, leading to systematic missingness (selection bias). This undermines single-task default prediction and motivates joint modeling as in RMT-Net, where the relationship between rejection and default risk is explicitly parameterized and exploited (Liu et al., 2022).
• Quality and Representativeness of Features: Early user-explanation datasets suffered from biased survey designs and unhelpful feature sets. Subsequent iterations refined question structure and post-hoc linguistic editing, resulting in more diverse, representative, and comprehensible explanations (Chander et al., 2019).
• Fairness and Ethical Assessment: When evaluating LLMs on serialized datasets, serialization formats themselves can influence both accuracy and fairness, sometimes increasing group-based disparities as measured by statistical parity or equality of opportunity (Azime et al., 29 Aug 2025). Balance in in-context learning examples, as well as routine stress-testing across formats and subpopulations, is necessary.
• Sparsity and Data Censoring: Crowdsourced explanatory datasets and survival data both exhibit sparsity—unique explanations or censored event times—which can challenge standard supervised learning algorithms and demand custom approaches, such as careful label aggregation, or algorithms designed to handle censored outcomes (Green et al., 7 Jul 2025, Chander et al., 2019).
• Temporal and Event Linkage: In survival and transactional datasets, robustly linking index and outcome events (e.g., loan open to loan close) requires precise and automated matching pipelines, capable of coping with partial repayments, early closures, or competing risks (Green et al., 7 Jul 2025).

5. Applications, Benchmarking, and Impact

Serialized datasets underpin a broad set of analytical and industrial use cases:

• Credit Scoring and Loan Approval: High-dimensional tabular datasets, processed via boosting or forest methods, enable rapid and accurate applicant triage, as demonstrated by near-perfect accuracy metrics in ensemble methods (Haque et al., 11 Oct 2024). Adaptation of DeFi-inspired tree-based scorers to more traditional, atomic loan datasets is feasible with attention to feature construction and time aggregation (Wolf et al., 2022).
• Survival Modeling for Risk and Timing: Survival pipelines provide estimates for expected time to repayment and default, supporting cash flow modeling, risk tiering, and tailored intervention strategies (e.g., proactive reminders) (Green et al., 7 Jul 2025).
• Explainability and User-centric Decision Support: Datasets emphasizing actionable, empathetic explanations bridge the gap between technical scorecards and the informational needs of consumers, and can be used to train, evaluate, and benchmark XAI algorithms (Chander et al., 2019).
• Fairness Auditing in Automated Decision-making: Systematic evaluation of performance and fairness metrics across serialization strategies and models identifies weaknesses in algorithmic systems and informs best practices for reliable, equitable model deployment in regulated financial contexts (Azime et al., 29 Aug 2025).
• Benchmarking AI Models: Open, large-scale benchmarks such as FinSurvival serve as test beds for new algorithms capable of handling high-dimensional, censored, and event-driven data in both DeFi and traditional financial domains (Green et al., 7 Jul 2025).

6. Future Directions and Open Issues

Several themes for ongoing and future work emerge from the recent literature:

• Expansion of Explanatory and User-friendly Datasets: There is an articulated need to extend the design philosophy of Xnet across domains, integrating features and explanation structures that adapt to specific stakeholder and regulatory contexts (Chander et al., 2019).
• Algorithmic Innovation for Sparse and Censored Data: Addressing challenges associated with sparsity in explanatory labels or high censoring in survival outcomes may require tailored machine learning and statistical methods, including new loss functions and uncertainty quantification (Green et al., 7 Jul 2025, Chander et al., 2019).
• Dynamic and Policy-aware Modeling: In environments where rejection/approval strategies evolve (multiple policies), architectures such as RMT-Net++ are highlighted for their robustness and adaptability (Liu et al., 2022).
• Fairness-aware and Interpretable LLMs: Building LLM-based systems for high-stakes domains requires not only best-in-class serialization and few-shot calibration, but also routine, multi-metric fairness auditing and, if possible, the development of LLMs specifically tuned for fairness objectives (Azime et al., 29 Aug 2025).
• Open Benchmarks and Scalability: Expanding public, modular datasets with rich features and event labels (as in FinSurvival) is vital to support community-driven benchmarking, reproducible research, and cross-domain algorithmic transfer (Green et al., 7 Jul 2025).

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In sum, serialized loan approval datasets enable high-fidelity modeling, explanation, assessment, and benchmarking across the operational spectrum of traditional and decentralized finance. Their continued evolution—along axes of feature design, fairness, scale, and user-centric explanations—remains essential to both scientific advances and responsible AI deployment in financial decision-making.