For all the discussion about the need for banks to digitalize, the talent shortage for them is a bottleneck without a straightforward solution
There’s now been a solid decade of gathering discussion surrounding digital transformation in banking, coinciding with the rise of fintech. In this time traditional financial institutions have taken steps to innovate and digitalize, with varying degrees of progress. Much of this has come about in response to regulatory developments, such as open banking, although other types of regulatory frameworks like MiFiD II have also compelled banks to upgrade their technological capabilities to improve compliance and reporting.
Data has become so ubiquitous though in general, that it is now the business asset that will drive virtually all financial services in one way or another. Banks are largely aware of the competitive and regulatory imperatives to digitize their business models, which is why they have been increasingly seeking to recruit professionals in data and artificial intelligence fields. However, there is a huge problem: banking demand for this talent is far outpacing supply.
For all the discussion about the need for banks to innovate and digitalize, the talent shortage for banks is a bottleneck without a straightforward solution. So what can banks do?
The will to innovate, without the talent to make it happen
Many financial institutions have a genuine desire to develop and run the kind of advanced AI and machine learning applications to truly transform their operations and processes. But the talent pool simply isn’t there to meet this demand, according to a global AI talent report.
Workforces are struggling to meet AI’s pace of change
First, the pace of AI advancement is faster than the global workforce can keep up with. Education and real-world experience takes years, by which time the technology will have evolved further, requiring further education and training, and so forth.
Huge recruitment competition
Second, financial institutions are becoming digital, cutting-edge technology enterprises. At the same time, technology companies are becoming financial entities too. For instance, Apple and Google both offer a range of financial services. What this means is that banks have to compete with high-tech organizations in other industries for the same talent. As a result, recruiting data scientists and AI professionals - already a field that is short on supply and long on demand - is a difficult, expensive endeavour.
Less appealing to workers
Third, financial institutions are traditionally risk-averse organizations. Embracing change and innovation is a relatively new concept to banks. After all, before fintech came along, the last truly innovative novelty from the banking industry was probably the ATM in 1969. As a result, AI professionals working for banks tend to have to work with and around legacy technology systems. In comparison, tech organizations like Facebook or Apple aren’t hamstrung by this sort of system setup.
While some professionals may like a more complex challenge, the reality is that legacy systems and banks’ traditional lack of agility likely makes them a far less attractive prospect for data professionals than employers that are technologically native, agile and truly innovation-minded.
Solving the data recruitment challenge with the right collaborations
Financial institutions can address their inability to source the data professionals they need in two ways, and they needn’t be exclusive.
Investing in employee AI and data training
One, ramp up investment in data and AI training and education for current employees and new hires. According to an IBM study, workers need 36 days to learn a new skill compared to three days in 2014, largely driven by AI and digitalization advancement. Meanwhile, a McKinsey report indicates that 9 out of 10 workers will require reskilling by 2030.
The alternative to a subpar internal training program for employees is risking falling behind competitors and an inability to attract and retain top talent. The investment to educate and upskill employees is considerable, but the cost of failing to do so is likely to far outstrip it.
Banks can focus on building the kind of training program that can produce the profile of AI professionals that they specifically are falling to recruit.
Identify the right specialist service providers
Investing in an internal training program is an important undertaking, however it requires three resources in copious quantities: financing, specialist human capital, and time.
Another approach is to address this talent deficit problem by seeking out and identifying the right third-party data specialist service provider. Doing so can provide banks with the intellectual capital they require to integrate AI-driven applications and use them to bring innovative products to market.
Between the two options - internal training and external collaboration - the former is expensive, time-consuming and complex to integrate, while the latter provides financial institutions with a ready-made, specialized solution and instant access to highly skilled, dedicated talent.
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