FINTECH MANPOWER DEVELOPMENT IN ASIA-PACIFIC FINANCIAL CENTERS WITH A FOCUS ON ARTIFICIAL INTELLIGENCE AND BIG DATA PROFESSIONALS

39 38 Comparing the Artificial Intelligence and Big Data Manpower Ecosystem of the Six Asia Pacific Financial Centers In Table 3.5, we break down AI and big data talent into three levels – junior, middle and senior - based on a broad and general classification of titles among AI and big data professionals in these cities. We focused on the top 100 most common titles in each city. Hong Kong has a relatively uniform distribution of professionals across the three levels. Singapore and Tokyo have the highest distribution at the senior level, with Singapore having the highest percentage of senior professionals among the six cities. This may reflect the fact that Singapore financial institutions attach a higher level of strategic importance to AI and big data. On the other hand, Shanghai, Shenzhen, and Sydney have the largest share at the middle level. Table 3.5 Proportion of AI and Big Data Professionals at Different Managerial Levels in the Finance Industry based on the Top 100 Job Titles City Junior Middle Senior Hong Kong 28.34% 36.60% 30.14% Singapore 21.96% 30.60% 41.24% Tokyo 18.42% 34.30% 37.58% Shanghai 29.06% 40.71% 24.13% Shenzhen 29.55% 43.05% 21.63% Sydney 19.25% 46.99% 27.25% In addition to managerial levels, we also did a breakdown of the titles among AI and big data professionals into technical and management positions based on a broad categorization approach (Table 3.6). Table 3.6 Proportion of Management and Technical AI and Big Data Professionals in the Finance Industry based on the Top 100 Job Titles City Management Technical Hong Kong 79.59% 16.33% Singapore 76.07% 18.94% Tokyo 72.55% 19.09% Shanghai 75.94% 18.70% Shenzhen 71.33% 23.21% Sydney 70.23% 25.77% For Tables 3.5 and 3.6, position titles were retrieved from LinkedIn profiles and were classified into managerial level (junior, middle, and senior) and function (management vs technical) by two independent coders who discussed and resolved any discrepancies between them. Note that the classification was based on general practices and did not take into account the specific circumstances and locations of individual firms. There were titles that were not classified into either category. This explains why the ratios add up slightly below 100%. 3.5 Academic Affiliations of AI and big data professionals in the financial industry As shown in Table 3.7, most of the AI and big data talent are graduates of local universities. Local universities understand the needs of their financial industries and can incorporate them into the program curricula. Students and graduates are also better informed about career opportunities via on- campus seminars, alumni engagement, and internship opportunities. Local universities remain the major source of AI and big data talent in the medium and long term for each city. Table 3.7 Top 5 Education Affiliations of AI and Big Data Professionals in the Financial Industry Hong Kong Singapore Tokyo Shanghai Shenzhen Sydney The University of Hong Kong National University of Singapore The University of Tokyo Fudan University Shenzhen University UNSW The Chinese University of Hong Kong Nanyang Technological University Waseda University Shanghai Jiao Tong University Sun Yat-Sen University University of Sydney The Hong Kong University of Science and Technology Singapore Management University Keio University Shanghai University of Finance and Economics Peking University University of Technology Sydney City University of Hong Kong Temasek Polytechnic Kyoto University Shanghai University Wuhan University Macquarie University The Hong Kong Polytechnic University University of London Sophia University Tongji University The Chinese University of Hong Kong Western Sydney University