Today, SCB is a leading universal bank, offering deposits and lending and a wide range of other products and services, to meet the needs of all customers. SCB aims to transform the whole digital banking to serve the easiest and fastest channel of banking to customers.
Under the vision to serve as "The Most Admired Bank,” SCB has established a strategic plan called SCB Transformation, designed to dramatically upgrade the Bank's infrastructure and enhance its long-term competitiveness. This transformative plan responds to rapid change in the financial services industry, which is being reshaped by digital technology, regulatory change and new consumer behavior. Following this strategic plan's "Going Upside Down" approach, the Bank will transform its business model and thinking throughout its human resources, work processes, products and technology, enabling breakthrough performance and creating sustainable value for our customers and society.
Once data is acquired, the crucial next step for a business is to build a team of people capable of extracting meaningful patterns from the huge pool of data using statistics programing ML AI. But this alone is hardly enough! They have to understand business requirements and think outside as usual.
Our team’s culture is similar toTech startups. Despite working in a well-established bank, SCB workplace is so much like a playground, with new projects coming in all the time and no routine tasks at all. New projects come out, our work is to find solutions.
As I recently join SCB, it totally changed my thinking on traditioanl banking. With SCB Work from Anywhere policy, smart casual dress code, and flexible working hours make me very comfortable working here. Importantly, the team is very open, allow me to throw out new ideas and fine new way of work.
Good data models help speed up other work processes, and reduce workloads or repeated tasks or issues. That’s why data geeks with expertise in statistics and machine learning have decided to work at banks.
We see things invisible to others through the gigantic volume of data beyond visual analysis. We can capture data relationship patterns amid such complexity with ML models and see the future before anyone else through predictive models. We can measure the confidence in the future fiqures/Model.