In this project, we are collaborating with Credit counselling Singapore (CCS) to develop an early warning system for default clients to help CCS act promptly and ultimately build financial resilience for individuals and community. SIT students had an opportunity to experience on how credit risk management with utilization of data analytics, is being carried out in the industry. 

Impact on students’ employability. 
Darrell Lim described the learning outcomes and impact on his career: “the CCS project has provided me with the required knowledge to forge into the field of management consulting –where machine learning and artificial intelligence shapes strategy and changes through various fields”

The project looked at improvement to a firm’s credit risk machine learning model to enhance the accuracy of a borrowers’ probability of default prediction, which benefited overall community financial resilience and shaped industry practices in credit modelling.

Do factors such as weather, haze, waxing and waning of the moon influence customer booking? In response to this question by The Cage, a sports facilities provider and an industry collaborator, an SIT team conducted a strategic review to identify the customer segments and their booking behaviour using Data Analytics. They assisted The Cage management with data-driven customer insights and identified areas of improvement in The Cage’s value chain.