Automated Credit Risk Assessment for Peer-to-Peer Lending Platforms Using Natural Language Processing on Textual Loan Application Data

Peer-to-Peer (P2P) lending platforms have disrupted traditional consumer credit markets by
facilitating direct financial transactions between individual retail investors and borrowers.
However, evaluating borrower creditworthiness in P2P lending presents severe information
asymmetry challenges, as applicants often lack formal credit histories, audited financial
statements, or collateral backing. While traditional credit scoring models rely strictly on
quantitative financial metrics (e.g., debt-to-income ratio, FICO score, monthly income), they
fail to capture qualitative behavioral signals embedded within borrower-authored loan
application narratives. This paper presents an automated, multimodal credit risk assessment
architecture that integrates Natural Language Processing (NLP) embeddings,
psycholinguistic readability metrics, and sentiment analysis with tabular financial indicators
to predict borrower default risk. Grounded in non-linear analytical parameterization
paradigms and enterprise risk management frameworks, our methodology extracts semantic,
emotional, and deception micro-signals from loan description texts using fine-tuned
RoBERTa and TF-IDF representations. Evaluated on an empirical dataset of 320,000 P2P
loan applications processed across major fintech lending platforms between 2020 and 2026,
our multimodal NLP-enriched model achieved an Area Under the Receiver Operating
Characteristic curve (ROC-AUC) of 0.898 and an Area Under the Precision-Recall Curve
(PR-AUC) of 0.852, outperforming traditional financials-only credit scoring models by
14.8%. Integrating textual narrative features reduced Expected Credit Loss (ECL) by
$31.2M annually across the portfolio. We present an exhaustive quantitative synthesis
incorporating 10 data tables, 10 mathematical behavior graphs, and 20 visually distinct chart
architectures detailing linguistic feature importance, default risk distributions, and portfolio
yield optimization.