Work

Dataset Comparative Study of Predictive Models for Non-Performing Loan Prediction in a P2P Lending Company

Year: 2026

Type: dataset

Abstract: This dataset contains structured P2P lending data for credit risk assessment and loan default prediction. It consists of borrower, business, financial, credit history, transaction, and repayment-related features, including revenue, EBITDA margin, debt-to-EBITDA, DSCR, business age, management experience, payment type, payor type, trade cycle, revenue growth, receivables status, and Pefindo credit history indicators. The dataset includes an npl_status target variable indicating the loan's non-performing loan (NPL) status. The dataset is intended for research on machine learning-based credit risk assessment and comparison of predictive models for P2P lending loan default.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23138625

Authors: Rahmah, Berliana Fadlilatur, Idris, Friskania, Azhary, Muhammad Amri Amrullah, Kristiana, Idha

Institution: Binus University

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7219652201

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