DETERMINAN INDIKATOR FINANSIAL DAN NON FINANSIAL DALAM ANALISIS KREDIT DI ERA BIG DATA (STUDI PADA P2P LENDING)

Putu Prima Wulandari (Accounting Department, Faculty of Economics and Business, Brawijaya University)
Nurlita Novianti (Accounting Department, Faculty of Economics and Business, Brawijaya University)
Aulia Fuad Rahman (Accounting Department, Faculty of Economics and Business, Brawijaya University)

TEMA (Jurnal Tera Ilmu Akuntansi)
Vol. 22 No. 1 (2021)

This study aims to predict and explain the effect of financial indicators on the credit score in the P2P Lending platform credit analysis and the effect of non-financial indicators on the credit score in the P2P Lending platform credit analysis. This research focuses on peer to peer lending transactions found on the fintech service "KoinWorks". This research was conducted based on the sample, and the sampling method was carried out in a non-probability sampling type, namely the judgment sampling method, and 36 companies were found that matched the criteria. The data used is secondary data derived from financial data and non-financial data of companies that apply for peer to peer lending services on "KoinWorks". While the data analysis technique in this study used descriptive statistical analysis, classical assumption test, and model accuracy test using SPSS for Windows version 16. The results of this study indicate that debt to equity ratio (financial indicator) and length of business (non-financial indicator) have an effect on determination of credit scoring by the platform.   Penelitian ini bertujuan untuk memprediksi dan menjelaskan pengaruh indikator finansial terhadap nilai kredit scoring dalam analisis kredit platform P2P Lending serta pengaruh indikator non finansial terhadap nilai kredit scoring dalam analisis kredit platform P2P Lending. Penelitian ini berfokus pada transaksi peer to peer lending yang terdapat pada layanan fintech "KoinWorks". Penelitian ini dilakukan berdasarkan sampel, dan metode pengambilan sampel dilakukan dalam tipe nonprobability sampling yaitu dengan metode judgement sampling, dan telah didapatkan 36 perusahaan yang sesuai dengan kriteria. Data yang digunakan adalah data sekunder yang berasal dari data finansial dan data non finansial perusahaan yang mengajukan layanan peer to peer lending pada "KoinWorks". Sedangkan teknik analisis data pada penelitian ini menggunakan analisis statistik deskriptif, uji asumsi klasik, serta uji ketepatan model menggunakan SPSS for Windows versi 16. Hasil penelitian ini menunjukkan bahwa debt to equity ratio (indikator finansial) dan lama usaha (indikator non finansial) berpengaruh terhadap penentuan kredit scoring oleh pihak platform.