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

37 36 Comparing the Artificial Intelligence and Big Data Manpower Ecosystem of the Six Asia Pacific Financial Centers 3.3 AI/big data-related vs. business/finance-related skills comparison among the six financial centers To understand the AI and big data skills of finance professionals, we estimated the proportion of professionals with (1) banking/finance-related skills only, (2) AI/big data-related skills only, and (3) both sets of skills in the finance industry. We first extracted the number of professionals in the finance industry based on the seven sectors of each of the six financial centers provided by Linkedln. This gives us the total population of finance professionals in each city. We then extracted common banking/finance-related skills from each financial center and aggregated them into a combined list. We identified the top 100 skills that are related to banking or finance only. By merging these 100 skills with the filter on AI and big data skills we presented earlier, we extracted the talent whose skills fall into one of the following categories: (1) Banking/finance-related skills only (without AI/big data-related skills) (2) AI/big data-related skills only (without banking/finance-related skills) (3) having both AI/big data and banking/finance skills Table 3.3 summarizes the analysis results. Within the finance industry, only a very small proportion of professionals possess only AI/big data-related skills, with Hong Kong having the smallest proportion. For Hong Kong, the proportion of professionals having both skill sets is a modest percentage of 9.41%. For all financial centers, the majority of the professionals possess only finance and banking related skills but not AI and big data skills. Table 3.3 Proportion of Professionals with AI/Big Data-related and/or Banking/ Finance-related Skills in the Financial Industry Proportion Hong Kong Singapore Tokyo Shanghai Shenzhen Sydney With only AI/big data- related skills 0.79% 1.05% 1.17% 1.15% 0.87% 0.86% With both sets of skills 9.41% 12.84% 7.46% 6.56% 3.72% 14.41% With only business/ finance- related skills 89.80% 86.11% 91.37% 92.29% 95.41% 84.72% 3.4 Categories of AI and big data professionals in the financial industry Table 3.4 shows the breakdown of AI and big data talent in the seven finance industry sectors. In general, the financial services sector has the highest proportion of AI and big data talent in all six cities. Banking is the second largest sector for all cities except Tokyo where Insurance ranks 2 nd . Insurance comes in 3 rd place for Hong Kong, Singapore, Shanghai, Shenzhen, and Sydney. For Tokyo, Banking ranks third. The investment sector ranks 4 th in all cities, while Investment Banking, Venture Capital and Private Equity, and Capital Markets rank from 5 th to 7 th in different cities. It is evident that the distribution of AI and big data talent depicts a fairly consistent distribution across the different sectors in the finance industry in the six cities. Table 3.4 Proportion of AI and Big Data Professionals in each Financial Sector Financial Sectors Hong Kong Singapore Tokyo Shanghai Shenzhen Sydney Banking 20.24% 35.32% 7.74% 12.90% 13.00% 17.63% Investment Banking 2.88% 0.78% 3.00% 7.58% 8.33% 0.61% Investment Management 7.98% 7.63% 6.48% 9.83% 7.84% 3.41% Venture Capital & Private Equity 2.34% 3.83% 6.56% 5.23% 4.54% 1.49% Insurance 10.71% 9.46% 16.33% 8.41% 8.77% 13.81% Financial Services 55.34% 43.68% 59.93% 51.55% 51.65% 63.56% Capital Markets 2.21% 1.11% 1.15% 6.02% 7.11% 1.05% Note: % of total is shown in the parentheses. It is worthwhile to note that the breakdown in Table 3.4 does not add up exactly to the total number of AI and big data professionals in the finance industry as shown in Table 3.3. This is because some LinkedIn members may hold multiple positions in the same firm, leading to more than one count of the same individual in the Report. However, the difference is small (< 2%) and does not have a significant impact on the general observations.