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Heriot-Watt University Malaysia (HWUM)'s School of Mathematical and Computer Sciences (MACS) had the privilege of hosting a talk titled Data Science – Industry Knowledge Sharing on 3 April 2024. In this rapidly evolving world of computer science, this sharing session was a unique opportunity for our esteemed students and staff to delve into new knowledge and gain a comprehensive overview of how data science is being harnessed in the industrial setting.
Our distinguished guest, Dr Chong Zan Kai, Head of Data Science from DCAP Digital Sdn. Bhd. He shared his profound insights in a presentation titled ‘Credit Scoring, Now and Then.’ Dr Chong Zan Kai, a renowned expert in the field, presented the evolving landscape of credit scoring, illuminating the potential biases and limitations inherent in traditional methods. The talk was primarily for the BSc (Hons) Statistical Data Science students, as well as students in Computing Science and Actuarial Science, as the latter can be involved in credit scoring as a career.
When we asked Dr Chong about his thoughts on data science and the world today, he said, “Data science has become an integral part of many industries today. As the field matures, we see a growing number of companies, including SMEs, embracing data science as a core part of their strategy to improve productivity and gain a competitive edge.”.
“Innovation in data science is still happening at a rapid pace. One notable example is the development of Large Language Models (LLMs), which have the potential to revolutionise various applications, such as natural language processing, content generation, and sentiment analysis. Continuous learning is crucial to remain relevant in this rapidly evolving industry; Data science professionals must keep up with the latest trends, techniques, and tools to effectively tackle the challenges and opportunities that arise.”
The talk provided a thought-provoking exploration of the future of credit scoring. Dr Chong’s interdisciplinary perspective sparked discussions on the intersection of statistics, computing, and data science in shaping a more equitable financial system.