An automated credit intelligence learning system

Tri Handhika, Siti Fatimah and Muhammad Farkhan Novianto

International Journal of Electronic Finance, 2022, vol. 11, issue 2, 87-100

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. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=122169 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijelfi:v:11:y:2022:i:2:p:87-100

Access Statistics for this article

More articles in International Journal of Electronic Finance from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().