Title: An automated credit intelligence learning system

Authors: Tri Handhika; Siti Fatimah; Muhammad Farkhan Novianto

Addresses: Centre for Computational Mathematics Studies, Gunadarma University, Depok, 16424, Indonesia; Data Science Department, Amartha Mikro Fintek, Jakarta, 12560, Indonesia ' Centre for Computational Mathematics Studies, Gunadarma University, Depok, 16424, Indonesia; Data Science Department, Amartha Mikro Fintek, Jakarta, 12560, Indonesia ' Centre for Computational Mathematics Studies, Gunadarma University, Depok, 16424, Indonesia; Data Science Department, Amartha Mikro Fintek, Jakarta, 12560, Indonesia

Abstract: To accelerate the financial services, microfinance requires tools and technologies to provide an automated dynamic credit decision which leads to an accountable and efficient system. Considering a case on loan disbursement in the micro-business sector, this study presents a very comprehensive innovation, namely automated credit intelligence learning system (ACILES) which consists of dynamic credit scoring and optimal dynamic credit pricing: derived from tenor, rate, installment and plafond (TRIP). While credit pricing is obtained from the profit based pricing and simulation process, the credit scoring is developed by modelling not only the borrower's profile, but also psychometric analysis of the perception of borrower and surveyor via item response model which is combined with multivariate adaptive regression splines (MARS) model and structural equation modelling (SEM), respectively. By performing the experiment, it is clearly proved that ACILES can be implemented in order to augment microfinance business capacity.

Keywords: automated; credit pricing; credit scoring; dynamic; learning system.

DOI: 10.1504/IJEF.2022.122169

International Journal of Electronic Finance, 2022 Vol.11 No.2, pp.87 - 100

Received: 16 Jul 2020
Accepted: 02 Oct 2021
Published online: 11 Apr 2022 *

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