REVIEW 11 cited by
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
read the original abstract
In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional credit scoring methods, however, often grapple with challenges such as narrow knowledge scope and isolated evaluation of credit tasks. Our work posits that Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks. To systematically explore LLMs for credit scoring, we propose the first open-source comprehensive framework. We curate a novel benchmark covering 9 datasets with 14K samples, tailored for credit assessment and a critical examination of potential biases within LLMs, and the novel instruction tuning data with over 45k samples. We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks. We evaluate CALM, existing state-of-art (SOTA) methods, open source and closed source LLMs on the build benchmark. Our empirical results illuminate the capability of LLMs to not only match but surpass conventional models, pointing towards a future where credit scoring can be more inclusive, comprehensive, and unbiased. We contribute to the industry's transformation by sharing our pioneering instruction-tuning datasets, credit and risk assessment LLM, and benchmarks with the research community and the financial industry.
Forward citations
Cited by 11 Pith papers
-
From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market b...
-
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications
BizCompass is a dual-axis benchmark evaluating LLMs on business knowledge in finance, economics, statistics, and operations management, linked to analyst, trader, and consultant roles, with public datasets released af...
-
FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
FinAuditing is a taxonomy-structured multi-document benchmark with 1,102 instances averaging over 33k tokens from XBRL filings, defining three tasks to evaluate LLMs on financial auditing capabilities.
-
CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
CLExEval introduces a human-annotated evaluation framework on 40 rare cases that identifies verbosity bias, hidden knowledge paradox, and 68.6% reasoning-to-output mismatch in LLMs while showing LLM-as-a-Judge overest...
-
Privacy-Preserving Credit Risk Prediction with Alternative Data
PrivacyCredit is a machine learning method that combines traditional and alternative data for credit risk prediction while satisfying privacy-preserving, model-confidential, and lossless properties.
-
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
COMPASS formalizes prompt engineering as a POMDP-based cognitive decision process for self-adaptive generation of task plan explanations via LLMs.
-
Debiasing LLMs by Fine-tuning
Supervised fine-tuning with LoRA on rational benchmark forecasts corrects extrapolation bias out-of-sample in LLM predictions for controlled experiments and cross-sectional stock returns.
-
Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
FinFRE-RAG combines importance-guided feature reduction with label-aware retrieval-augmented generation to boost LLM performance on tabular fraud detection across four public datasets while providing human-readable ra...
-
Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
A benchmark of 150 AutoML competition tasks built by a web-agent pipeline, with leaderboard-derived difficulty labels and multi-metric evaluation.
-
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
CausalGAN + SAC RL pipeline generates synthetic bond yield data; fine-tuned Qwen2.5-7B LLM produces trading signals, with reported MAE 0.103, 60% profit rate, and LLM score 3.37/5.
-
Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.