Enhancing decision making with machine learning: the case of aurora crowdlending platform

Pearpilai Jutasompakorn, Arif Perdana, Vivek Balachandran

Research output: Contribution to journalArticleResearchpeer-review


The crowdlending industry is a fast-growing financial technology (fintech) sector that brings together borrowers and lenders. As an alternative financial intermediary, the crowdlending industry plays an essential role in reducing the financial exclusion of small and medium-sized enterprises (SMEs) struggling to obtain funds from traditional financial intermediaries such as commercial banks. With the onset of Covid-19 and the deteriorating economies worldwide, Singapore crowdlending platforms have come under pressure due to the increasing default rate of their borrowers. This case study illuminates the challenges faced by Aurora (pseudonym), a crowdlending platform that operates in Singapore, Indonesia, and Malaysia. In response to high default rates during Covid-19, Aurora’s management made improvement to its current machine learning-based credit scoring model in June 2021. This case study describes the challenges Aurora faced in identifying relevant features for the machine learning model, data preparation and cleansing, and selecting the appropriate credit model algorithms to replace its current approval process.

Original languageEnglish
Pages (from-to)58-66
Number of pages9
JournalJournal of Information Technology Teaching Cases
Issue number1
Publication statusPublished - 18 Feb 2022


  • machine learning
  • analytics
  • credit scoring
  • crowdlending

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