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View corpus contextA hybrid system combining knowledge graphs and retrieval‑augmented LLMs predicts loan defaults more accurately than traditional credit models on a Lending Club dataset, and retains stability under market shifts and cross‑industry tests; however, evidence is limited to dataset experiments and requires real‑world deployment validation.
Construction of Financial Risk Assessment Model Based on Text Mining and LLM Architecture
openalex
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low evidence
7/10 relevance
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An end‑to‑end risk assessment system that fuses text mining, a risk knowledge graph and retrieval‑augmented LLMs improves credit default prediction on the FinBen Lending Club dataset, outperforming conventional structured and plain‑text models and showing robustness across simulated market and cross‑industry tests.
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Faced with the challenges of accelerating growth in unstructured text data and increasing risk concealment in the financial market, this study constructs a financial risk assessment system that combines text mining with large language models (LLMs). This system forms an end-to-end architecture encompassing data, knowledge, models, services, and governance. The system collects multi source text, constructs a risk knowledge graph, and extracts key events and sentiment signals. These are then integrated into the LLM framework of retrieval augmented generation (RAG) and multi feature fusion to achieve credit risk prediction and default probability estimation. This experiment relies on the FinBen Lending Club dataset (2024) and conducts comparative experiments, ablation studies, error analysis, and stability tests. The model outperforms traditional structured models and plain text models in key evaluation indicators such as F1, MCC, and PR AUC. In scenarios of market environment changes, cross industry migration, and anti-interference, the model’s stability and compliance performance are outstanding. This study designs an intelligent risk identification solution for financial institutions, which makes the identification process explainable, traceable, and auditable. This study has significant theoretical and practical impact on risk governance and decision support for banks, securities, insurance, and regulatory authorities.
Summary
Main Finding
The study develops an end-to-end financial risk assessment system that fuses text mining, a risk knowledge graph, and large language models (LLMs) within a retrieval-augmented-generation (RAG) + multi-feature fusion architecture. Applied to the FinBen Lending Club dataset (2024), the system improves credit-risk prediction and default-probability estimation, outperforming traditional structured-data models and plain-text LLM baselines on key metrics (F1, MCC, PR AUC). It also demonstrates strong stability and compliance (explainability, traceability, auditability) under market shifts, cross-industry transfer, and anti-interference tests.
Key Points
- Architecture: end-to-end stack covering data collection, knowledge construction, modeling (LLM + RAG), services, and governance.
- Inputs: multi-source unstructured text (news, filings, social signals, etc.) combined with structured financial features.
- Knowledge component: constructs a financial risk knowledge graph (entities, relations, events) to encode domain knowledge and support evidence grounding.
- Signal extraction: extracts key events, temporal information, and sentiment signals from text to enrich feature space.
- Modeling approach: integrates RAG (retriever + LLM generator) with multi-feature fusion to combine retrieved textual evidence, KG signals, and structured features for downstream credit/default prediction.
- Evaluation: comparative experiments vs. structured-data models and plain-text LLM models; ablation studies, error analysis, and stability tests (market environment change, cross-industry migration, anti-interference).
- Performance: consistent improvements in classification quality and ranking (higher F1, MCC, PR AUC) and robust behavior under distribution shifts.
- Explainability & governance: design emphasizes explainable, traceable, and auditable predictions via retrieval evidence and KG paths—supporting regulatory and institutional needs.
Data & Methods
- Dataset: FinBen Lending Club (2024) — used for training, validation, and testing of prediction tasks (credit risk / default probability).
- Pipeline (high level):
- Multi-source text collection and preprocessing.
- Event and sentiment extraction from text (to capture risk-relevant signals).
- Construction of a financial risk knowledge graph linking entities, events, and risk relationships.
- Indexing documents and KG fragments for retrieval.
- RAG integration: a retrieval module supplies evidence to an LLM generator; the generator produces representations/interpretations used for prediction.
- Multi-feature fusion: combines LLM outputs / retrieved evidence / KG-derived features / structured numeric features into downstream classifiers that output risk scores / default probabilities.
- Experimental methods: baseline comparisons (structured-only models, plain-text LLMs), ablation experiments removing KG or signal types, error-case analysis, and stress tests for robustness (market shifts, cross-sector transfer, adversarial/perturbation tests).
- Evaluation metrics: classification and ranking-oriented metrics (F1 score, Matthews Correlation Coefficient (MCC), Precision–Recall AUC), plus stability/compliance assessments.
Implications for AI Economics
- Improved risk pricing and allocation: richer text-derived signals and KG grounding can reduce information asymmetries and help lenders and insurers price credit and insurance more accurately.
- Early detection of concealed risk: event-based extraction and KG evidence improve detection of opaque or hidden risks that structured data alone miss, lowering unexpected losses and moral hazard.
- Robustness to regime change: demonstrated stability under market/environment shifts supports more reliable economic decision-making and stress-testing.
- Regulatory and governance benefits: explainability and audit trails (retrieved evidence + KG links) help compliance, model validation, and supervisory review—reducing regulatory friction for LLM-based systems.
- Cross-sector applicability: architecture is applicable across banking, securities, and insurance for credit scoring, counterparty risk, surveillance, and systemic-risk monitoring.
- Research and policy directions: encourages integration of symbolic knowledge (KGs) with LLMs for economically consequential predictions; raises questions about data governance, model drift, fairness, and privacy that should be addressed for widescale deployment.
(Note: the summary reflects the study-level design, evaluation strategy, and qualitative outcomes described. Exact model architectures, hyperparameters, and numeric performance figures were not provided in the source text.)
Assessment
Paper Typedescriptive
Evidence Strengthlow — The study reports improved predictive performance on a single proprietary/augmented dataset (FinBen Lending Club 2024) using cross‑sectional experiments, ablation and stability tests, but provides no causal identification, field validation, or out‑of‑sample deployment evidence; results may reflect dataset-specific tuning, label leakage, or optimistic evaluation practices rather than generalizable real‑world gains.
Methods Rigormedium — The paper uses standard ML evaluation elements (comparative baselines, ablation studies, error analysis, robustness checks), which supports internal validity, but key methodological details appear unspecified or uncertain (data preprocessing and provenance, sample sizes and splits, hyperparameter tuning, class‑imbalance handling, baseline selection, calibration, fairness checks, and code/data availability), limiting reproducibility and confidence in external validity.
SampleLoan‑level records from the FinBen Lending Club dataset (2024) combining structured borrower/loan features (e.g., income, credit attributes, loan amount) with multi‑source unstructured text (application text and other sourced documents), with binary or probability labels for default/credit outcomes; exact sample size, time window, and provenance of the text sources are not reported in the summary.
Themesgovernance innovation
GeneralizabilitySingle dataset (Lending Club/FinBen) — may not generalize to other lenders, borrower populations, or countries, Possible proprietary or augmented text sources limit replicability and external validation, Model may rely on language or corpus artifacts (language, platform) not present in other contexts, Claims about regulatory compliance and auditability need real‑world deployment tests, Performance under future market regimes or adversarial data manipulation is uncertain despite reported stability tests
Claims (9)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| We construct a financial risk assessment system that combines text mining with large language models (LLMs) and forms an end-to-end architecture encompassing data, knowledge, models, services, and governance. Other | positive | system_architecture (end-to-end integration) |
Reading fidelity
high
Study strength
low
|
not reported
|
| The system collects multi-source text, constructs a risk knowledge graph, and extracts key events and sentiment signals. Other | positive | extraction_of_events_and_sentiment_signals |
Reading fidelity
high
Study strength
low
|
not reported
|
| Extracted events and sentiment signals are integrated into an LLM framework using retrieval-augmented generation (RAG) and multi-feature fusion to achieve credit risk prediction and default probability estimation. Decision Quality | positive | credit risk prediction / default probability estimation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The experimental evaluation relies on the FinBen Lending Club dataset (2024) and includes comparative experiments, ablation studies, error analysis, and stability tests. Other | positive | experimental_evaluation_protocol |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The proposed model outperforms traditional structured models and plain text models on key evaluation indicators such as F1, MCC, and PR AUC. Decision Quality | positive | classification_performance_metrics (F1, MCC, PR AUC) |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Under scenarios of market environment changes, cross-industry migration, and anti-interference, the model demonstrates outstanding stability and compliance performance. Decision Quality | positive | model_stability_and_compliance_performance |
Reading fidelity
medium
Study strength
medium
|
not reported
|
| The study designs an intelligent risk identification solution for financial institutions that makes the identification process explainable, traceable, and auditable. Ai Safety And Ethics | positive | explainability_traceability_auditability |
Reading fidelity
high
Study strength
low
|
not reported
|
| The approach has significant theoretical and practical impact on risk governance and decision support for banks, securities, insurance, and regulatory authorities. Governance And Regulation | positive | impact_on_risk_governance_and_decision_support |
Reading fidelity
high
Study strength
speculative
|
not reported
|
| Compared to plain text models, integrating knowledge graph signals and multi-feature fusion in an LLM-based RAG framework improves default probability estimation. Decision Quality | positive | default_probability_estimation_accuracy |
Reading fidelity
medium
Study strength
medium
|
not reported
|